Hello, and welcome to the AI Engineering podcast, your guide to the fast moving world of building scalable and maintainable
AI systems.
Your host is Tobias Macy, and today I'm interviewing Donnie Greenberg about Runhouse and the current state of ML and AI infrastructure. So, Donnie, can you start by introducing yourself?
Yeah. Glad to be here. I'm,
Donnie. I'm the, cofounder and CEO of Runhouse.
We are a serverless distributed compute platform for AI and ML.
I've been working on the project for about 2 years now.
Prior to that, I was the product lead for PyTorch at Meta. I worked on almost every aspect of PyTorch,
and some large rearchitectures
of Meta's internal AI platform. So that's kind of
informed a lot of the views that I have of AI, ML infrastructure, and the nature of Run House today.
And do you remember how you first got started working in the area of ML and AI?
Yeah. Actually,
I came from more, like, research and open source. So I was at IBM Research working on
quantum computing applications,
and, I was the tech lead for their open source quantum algorithms software.
And then,
PyTorch team reached out
because being a kind of research
and open source,
centric system,
they they wanted
product help that was going to be coming from from from that side of the world.
And so I actually came into the PyTorch team with only really
quantum machine learning background, but had to quickly ramp
on basically every domain within, within PyTorch,
and then
lots of the kind of,
lower level subsystems,
like compilation, distributed, etcetera, and then, you know, gradually had to kind of broaden out into the higher level subsystems, like,
orchestration
and,
fault tolerance, those kinds of topics to kind of,
the broader machine learning life cycle.
And from your perspective of working in the space, working at Meta, where you've got exposure to very large scale and complex AI and ML workflows,
what do you see as the core elements of infrastructure for these ML and AI capabilities
and applications and maybe some of the ways that that's changed over the past few years?
Yeah. It's an interesting question because it's, like, it's changed a lot over time, and it really depends
who you're asking.
I worked
with many internal meta teams, and meta
has very custom kind of homegrown infrastructure, but then also
worked with the,
large vendors of AI infrastructure and then many teams in enterprise that were building up their own stack.
And it's sort of a you know, there are multiple waves that are actually moving in sequence,
or moving in parallel with each other. So I think it's actually really important at this particular moment in time to kind of distill
exactly what AI machine learning infrastructure is, especially as it's distinct from just, like, traditional infrastructure.
I think it was a very easy answer, which is just, like,
ML and AI teams do some specific things. So let's just, like, emulate those activities and wrap them in, like, an API that you can call that does the thing for you,
and we'll call that AI ML infrastructure.
But that always comes with very significant opinionation. So I would describe kinda like the first wave of AI ML infrastructure kind of as that way. It's just like very monkey see, monkey do kinda grab bag of of of point solutions to various problems in the AI ML life cycle, and then we've gotten more mature from there. So, like, all the opinionation that that adds, right, like, when you're just wrapping a thing that somebody does in a an API to do it for them,
you're gonna take on a lot of opinionation. And even if you're more and more thoughtful about it, it's very difficult to reopen doors that you've closed,
in just, like, the interface of the API is proper. And, also, like, when you're doing this for many, many companies, you often introduce features that look innocent at the time, but actually introduce a lot of opinionation because
the feature itself is optimizing some aspect of the AI ML life cycle, which depends on the methods proper.
So
I think
after the first wave of very opinionated tools, you ended up kind of with much, much more unopinionated
tools that were built kind of in these, like, homegrown enterprise stacks. And,
those included, I would say, more of a kind of, like, core grab bag of elements, like a Kubernetes cluster, an orchestrator,
some kind of serving solution.
But even those had a degree of opinionation, and they were often very focused on actually just getting things working on the production side.
So as far as the, like, core core elements, I think today,
where where we've gotten to just continuing to try to strip away that opinionation
is that
the the the really, really distinct aspect of AI ML that we know for sure is that there's large scale
data and computation
that can't be done on a laptop, and therefore, it doesn't have a local development path. And so if you, like,
remove everything else, remove all of the, like, AI method specifics, all of the specific choices of distributed frameworks and things like that, that's a really key piece that hasn't been solved, or that that hasn't been solved in the core infrastructure that you need to introduce with AI ML infrastructure,
and you can kinda blow out the whole world from there. So, like,
being able to run things on GPUs
requires you to be able to have, like, a really interactive dev experience on remote compute,
and be able to schedule that computation in some sort of shared compute environment with other people. The scale of the data not fitting in a typical
laptop or even like a, you know, single dev box requires you to be able to account for kind of stream ability of of your
workflows from 1 sort one, you know,
place of compute to another place of compute or just be able to size up and down your workflows very dynamically.
But just that lack of local
execution demands basically a sort of platform as a runtime.
So I think that's where most of the world has gone or or most of, like, bleeding edge of AI ML is gone. Is this, like, platform or, as a runtime concept?
And then one other thing that I would point to is distinct about AI ML, but has sort of glimmers from the day there there are glimmers of this in the data world
is just, like, super, super wide
workflows because they incorporate both online and offline elements.
So, you know, most of the web world looks very online. Right? Like, live serving systems, very continuous curves as far as the traffic and the scaling. A lot of the data world is, like, very offline where, you know, jobs that are being run nightly or have, like, you know, multiple dependencies, but many of which can kind of wait but have to complete eventually.
AIML tends to mix those in a way that, requires, like, very, very wide systems to be programmed and, like, fall tolerantly
and reproducibly. And and that's another thing that hasn't completely been solved by either the website or the data side that AI ML infrastructure tends to be focused on. And the curve ball that has really come in the last couple of years is the shift of a lot of the ML and AI focus from deep learning and your traditional linear aggression style ML workflows to now everything is transformer models,
building off of these large language foundation models or multimodal models and being able to serve those for inference largely with some sort of context corpus.
And I'm wondering how you're seeing those pressures affect the ways that people are thinking about their overall infrastructure and workflow stacks for being able to support their ML and AI objectives,
And how much of the generative AI space is hype and people are still doing all of the deep learning and linear regression style ML?
And how much of it is that has actually taken over a substantial portion of what people are focused on right now?
Yeah. I think it's actually
it's become a lot clearer over the last few years what exactly is AI and what exactly is ML.
And,
and and then what exactly kinda falls into, like or distinguishes that from, like,
classical, like, big data work? So I would say, like, classical big data work is, like, mobilizing data inside of a company for the purpose of, like, strategic decision making and analytics.
And ML has always been distinct from that in that it is mobilizing
data at scale within a company for the improvement of product proper.
Right? So
that really introduces a hard fork, you know, in in those stacks. Right? The
mobilization of the data itself, that that can mean deep learning. It can mean regression, whatever. But that really means that you're actually using the first party data in a way that, adaptively improves your own systems. Now I think we have a lot more clarity actually about what is distinct like, what distinguishes ML from AI, because
AI mostly seems to be taking the form of
machine learned or intelligent methods
that have a kind of globally shared distribution about them. Meaning, like, the model itself actually represents a concept that is
transferable from one person to the next, like, you know, language or identifying cats and images, etcetera.
Whereas, like, ML, especially as we see it inside of large companies, is very much
proprietary distributions. Right? It's like mobilizing my transaction data to build a fraud detection system, mobilizing my
user interaction data to build a ranking over my own products or recommendation,
or search system over my own space of entities. And that's actually a really,
important distinction to make because the infrastructure that you need tends to look very different for each of those two things, just as the infrastructures look very different between BI and ML.
So on the AI side, you might just be able to use large off the shelf hosted model because
language is language, and the shared distribution is, you know, common between you and the person who trained the model. In the end, the the behavior of the model still depends on the data that it was trained on, and so the importance of post training thoughtful post training by these hosted model providers will actually end up dictating its applicability to you as a consumer, whether the behavior of the distribution they're modeling is actually gonna be useful. If they built it for chat, it's gonna be built useful for you for, like, unstructured data abstraction,
over
some very specifically formatted PDFs. But, ultimately, like, you know, you want to bin pack these jobs as aggressively as possible. Maybe you're hosting your own models that can kind of be, like, widely shared within the company or something like that. On the ML side, we're still seeing a tremendous amount of customization of things that you want to do because,
ultimately,
these are systems problems with, you know, dozens or hundreds of engineers banging on the same system, just incrementally improving it, and it's much more of a sort of scientific activity
and one that needs to just be, like, kind of optimized over time.
And so the range of activities that people are doing there is actually going in the opposite direction. On the AI side, they tend to be converging. Even the large models that you see off the shelf tend to be actually converging in behavior,
especially over the last 6, like, 6 months. On the ML side, it's, if anything, significantly more divergent. It's like there's still a tremendous amount of, you know, enterprise
business that depends on traditional ML models and nothing more, like, you know, regression, gradient boosted decision trees, etcetera.
And then, you know, there's just extreme pressure
to walk up the chain to more and more sophisticated models, either using,
deep learning,
transformer architectures, larger and larger models, larger and larger training techniques. Right? And then
that pressure manifests as a sort of, like, distribution of where people end up in terms of how much how much sophistication they can introduce. So maybe you have a business that depends on gradient boosted decision trees today, and you just desperately wanna get that onto, like, you know,
GPU hardware. And then maybe you wanna actually once you're on GPU hardware, you just desperately wanted to get into deep learning to have more sophistication and just yield better performance.
And then once you got you you got it onto single GPU deep learning, you wanna get it onto multicard or distributed or to, let's say, like, a large scale architecture, like a distributed
transformer training or something like that. So that pressure has actually just scattered people across the range of sophistication.
I think there's one area that's been quite interesting that's where where they've overlapped, and ML and AI have arguably always overlapped here, which is where you're combining,
you know, shared distributions and shared models, like a transformer, like an embedding,
with mobilizing first party data to build ML systems. For example, you know, instead of doing lots of really elaborate complex featurization,
in order to train, let's say, like, a fraud detection model, maybe you take your text that, you know, arrives in your Tableau data, and you shove it into a massive embedding. Do a hosted model provider or your own hosted open source model and get that embedding, and then use that to train your machine learning model. And, like, that's really the sort of hello world example of that, but it gets, you know, arbitrarily complex from there. But that's arguably kind of more of the same. That's always been the job of machine learning practitioners to take
all the new model architectures and whatever they can to try to improve their machine learning systems. But that's kind of like the the the wrinkle between the 2 where lots of teams that have been doing ML started to walk up the AI stack and incorporating more.
Another interesting aspect of the ML infrastructure space is that I think maybe around 2019,
2020, there was a large investment in the overall idea of MLOps and
building largely a separate stack of infrastructure and suite of tools for that machine learning practitioner,
distinct from the work that was being done in data engineering.
And I think part of that was exploring the space of what are the tools that ML engineers need? How does this work? How do we build this as a service and as infrastructure?
And I'm wondering what you have seen as some of the lessons out of that MLOps tool growth and the corresponding tool growth and explosion that happened in data engineering around that same time frame,
and any ways that those two areas are starting to converge,
or any lessons learned that are shared between those workflows?
Yeah. So I I actually I think that this is
the the MLOps phenomenon
really points to another really key distinction
that should be drawn in the way that the that we talk about the infrastructure.
AI infrastructure
can be kinda pointed to as somewhat different from, like, ML infrastructure,
where if you're talking about, like, AI infrastructure at scale, often that means that the definition of done is training and deploying one really huge
model that models this particular shared distribution for all the people who are gonna be consuming this model.
And the fault tolerance necessary for training, for example, doesn't need to be that strong. Like, most of these,
AI labs that are producing these models
are triggering trainings mostly by humans pressing enter on a laptop,
and the trainings themselves are quite homogeneous. They're massive and quite homogeneous,
and they work perfectly fine on Slurm. And that's the architecture you've actually seen
things sort of head towards in, like, a in an AI lab is just being able to
share, compute over
relatively massive like, over massive relatively homogeneous jobs, whereas ML infrastructure has always had very, very different requirements from that. You're talking about models that work best when they're trained often, when they can incorporate or remodel the distribution as often as possible. So sometimes they're trained as often as every hour, or
10
minutes
we've seen, and that tends to require a much richer fault tolerance, much, much richer support for, like, complex fault tolerance,
much richer automation,
observability, telemetry, etcetera. It's just a totally different scale dimension. Right? So instead of just running one massive training every 3 months, you're running, you know, 1,000 or 100 of 1000 or millions of trainings,
and they're they all look slightly different. They're super heterogeneous.
So I think that's a really important distinction to make, and MLOps is almost entirely about solving
the latter problem and not the AI lab problem. And I would actually just treat the AI lab problem as kind of completely separate for the purpose of this discussion.
The
MLOps phenomena, I think, actually created kind of a lot of short scorched earth mainly because there was so much mixing of mixing of these,
companies together the requirements of these companies together. They were in different stages of the distribution.
So from, like, 2018 to 2020, a lot of, like, the large enterprises
were really in the experimentation stage of deep learning.
They were just starting to realize real value in pockets of the organization,
and therefore
mainly wanted infrastructure that kinda was monkey see monkey do is going to, like, dependably give them a solution for a very specific problem they have and not really give them so much control and customization over the infrastructure or the methods themselves. But But adding into, like, 2020 to 2022,
you tended to see many of those companies actually outgrow that opinionation,
have maybe deep learning now producing real business impact in multiple different organizations. And so there was a push for consolidation. And the different methods that were used in those different stacks now just directly conflicted with the opinionation inside of
a platform in a box type solution like SageMaker or Vertex AI or something like that. So, you know, when you mix those 2 audiences together and you just group them into it under a term like MLOps, you're not gonna have a good time. It was like very different requirements.
And ultimately,
even, you know, the bleeding edge kind of practitioners in the space also have slightly different requirements than the ones who are just building their homegrown stack for the first time, and they just, like, wanna get it working. You know, they don't need
extreme,
flexibility to be able to mix workflows between,
massive
data processing systems and then massive distributed training or fine tuning or evaluation or inference systems, like, super heterogeneously.
So I think on the data side, actually, there tended to be pretty extreme
standardization of these workflows in a way that everybody benefited. Like, the very overused term, like modern data stack, I think is a reflection of the fact that people converge pretty quickly to the standard activities that people wanted to do. On the ML side, we just completely didn't have that. And
I think one of the biggest differences that you see now between the two sides is that there's this massive emphasis on the data side about, like, standardization,
doing everything in platform, these kind of walled garden,
data estate, or lake house kind of architectures.
And the benefits you get from them are huge. You've got, you know, amazing lineage. You get amazing off benefits. Right? You just, like, spin up an environment as a data practitioner, and all the data that you might need or have access to is just kind of authenticated and at your fingertips. On the ML side, we just totally don't have that. Like, arguably, on the ML side, we're completely still in is almost like Hadoop era where
anything that you wanna do that isn't essentially single node, you can't, like, SSH into,
a VM to do. It's just completely in, like, this wild west. And then that's if you're already somewhat familiar with the workflow and a bill and, like, able to unblock yourself. If you're in
a different part of the organization where you're just starting to adopt ML and you're looking at other people who have already adopted it,
then, you know, you're completely lost. Right? Like, there's absolutely no standardization. There's no in platform kind of support,
and you don't have any of this sort of democratization
that's happened on the data side where, like, many people inside of an organization can be tapping into these tools and have the data at their fingertips and even just, like, dabbling in that kind of data work. So I think that the the place where that is the most glaring
is inside of inside of those data states. So, like, inside of Snowflake or inside of Databricks, obviously,
there's many, many solutions for, like, data centralization,
you tend to see ML teams actually doing a bunch of their data work in platform. Like, maybe they do their exploratory data analysis in notebooks. Maybe they do a bunch of processing using, like, SQL or Spark or, you know, whatever whatever they do use for their, like, big processing. And then they get up to the training stage, and they have their sort of batches, and they just, like, crash out of the platform
to use whatever they use for training.
And in some cases, we see people move like, bouncing back and forth between these kinds of platforms,
and it's just, like, glaringly bad. Right? Like, the fact that, like, you have this, like, beautiful auth and telemetry and, like,
unification about the admin of administration of the compute and all that kind of stuff. And then just, like, right when you get up to training, you, like, crash out. And training is such a fundamentally
data
activity. Like, it just sort of it that that I think is the most glaring place where you can see this, like, misalignment or how far behind we are on the ML side from where they are on the data side.
And on that point of trying to unify the experience for ML and data teams, I know that there are projects like Metaflow that came out of Netflix and ML flow from the Databricks folks. And then there are these massively parallel compute frameworks like ray and Dask that aim to alleviate some of that pain.
And I'm wondering if you could talk to some of the ways that you think about the problems that they're trying to solve and some of the ways that maybe those conflict with the requirements and the needs of those ML teams that have those very heterogeneous
workflows and wide distribution of needs.
Yeah. So I think the
the industry
has gradually marched in the direction of less opinionation,
but I would call it a sort of greedy march so far where,
you know, people are
kind of throwing their hands up and just declaring, like,
our platform doesn't accommodate the bleeding edge of deep learning. We need to rebuild. And this happens at
the the the digital native kind of, like, AI first companies. It happens, like, every
2 to 3 years that they need to, like, rearchitect their platform to accommodate
a wider set of methods than or or or to kind of nuke some of the assumptions about AIML that they made in the beginnings of their platform.
And so, like, many of the systems that you mentioned are somewhere along that march of, like, eliminating opinionation. So,
you mentioned, like, Ray and Dask. Like, I think that those are good examples of, like, saying, okay. Our distributed
frameworks need to be significantly less opinionated and maybe just like Python generic. And I think that's generally a good direction. They serve as, like, a really strong foundation for
abstractive
libraries
to be, deployed to many different organizations
and to kind of just introduce less opinionation in the lower level
upon which those those libraries are built. I would still say that we're not completely there. Like, I still think that almost every choice that a machine learning team has available to them for how they want to
architect their kind of compute foundation has a lot of opinionation relatively, especially, like, relative to the data side. So to us, like,
the some some really amazing experiences that you have on the data side are, like, Databricks Spark, where just, like, in code,
you declare, like, hey. I need this size cluster.
I need this size.
I I need this much compute. And then you take, like, the work that you have to do that you're gonna basically, like, parallelize,
and you just throw it at that cluster, and you point at whichever data you're going to process, and it, like, doesn't. And the unopinionation that you have there that's worth noting is, like, you could do that from anywhere. You could do it from a local IDE. You could do it from inside of a notebook outside of the Databricks
ecosystem. You could do it from in a notebook in the Databricks ecosystem. You could do it from Argo or an orchestrator in production. You could do it from,
pod deployed in Kubernetes. Right? It's really an opinion about the place where you actually do the work. By comparison,
deep learning systems do not have that type of affiliation. So
if you're using Ray or Dask or you're using Dask isn't exactly a deep learning system, but, like, it's quite popular.
If you're using those or if you're using even systems like SageMaker training or Vertex training, they're very opinionated about how you execute the code. And if they are somewhat remote first,
then they're still basically just making you submit a CLI command. So, ultimately,
the code is running kind of in the place where they consider it to be safe to
land your land your, your your code to execute.
So in the case of Ray, for example,
you really want to be actually, like, triggering the execution from on a Ray head node. So what that often means in terms of the workflows is like SSH ing into array cluster
or connecting, like, a hosted notebook to the array cluster or submitting a CLI command so it only executes from on array cluster. Like,
that's pretty disruptive. Right? And then ultimately, that means that in production, and you've seen the stack's gonna go this way for people who have adopted a ray, your orchestration node needs to launch
or your orchestrate your your, step in your orchestrator needs to launch a ray cluster and not just like a regular,
you know, container or whatever that may be. And so I think that that's
been that's been a big phenomenon
lately is, like, maybe getting away from some of the problems of MLOps where research and production are super, super far apart. You have this extreme fragmentation between the stuff that you can do at real scale on your platform as a runtime type system where you're running your production jobs, and then you're unblocking your researchers in notebooks. So, like, Ray has been amazing for that, giving people kind of unified
runtime that that will work for both, but then introducing a lot more opinionation. Metaflow, I think, is kind of another great example where
it solves a really core problem
where
the mixed execution
of your kind of Python code that's running locally that's maybe not so demanding, and then the super demanding stuff that needs to run on your platform as a runtime,
can be kind of interleaved. But, again, it's, like, really opinionated. So, you know, it you need to kind of structure your workflows
in this, like, work this opinionated workflow kind of structure.
And if you want to use your platform in a way that doesn't exactly fall into kind of that DAG based sequence, then,
you know, tough luck. That's kind of just the way that the system has been built. So I think moving away from that is something that that we see as, like, a secular trend. And then I think that there are also, like, very serious fault tolerance implications to that opinion So if you are executing all of your code
as, let's say, Ray or DASK jobs
within a cluster structure
or you're executing all of your code within,
let's say, a DAG structure given to you by an orchestrator,
by definition, you don't have full control and creativity
over the way that you handle faults. So in the case of running your code inside of a cluster structure like Ray or Dask, if something fails on the cluster, like a node goes down or a process fails or something like that, failures tend to be cascading. It's like a very coupled structure,
and you can't handle that from inside of the cluster itself. Right? Like, if something fails in the cluster,
like, it's practically
gone. Right? You, like, run out of memory and some on a head note or something like that, it's gone. Or in the case of,
like, an an orchestrator,
if something goes wrong in the sequence of orchestration that that orchestrator has not built
the operator or the support for you to explicitly handle,
then you're kind of done for. You don't have the ability to kind of just, like, fully creatively within the context of a normal programming language, be able to catch an arbitrary exception, be be able to handle lots and lots of kinds of edge cases, be able to have some sort of failure on the cluster, and then basically say, oh, I recognize that. I want to just, like, nuke the cluster and do something different.
And so, like, we we still see those as very significant gaps on the ML side. Right? This is, like,
super, super arbitrary kind of fault tolerance, super heterogeneous
workflows in the way that you actually utilize the clusters.
And now bringing us to what you're building at Runhouse, can you give a bit of an overview about how you're addressing these challenges of ML infrastructure, how you think about the workflow that is ideal for these teams that have such disparate needs, and some of the core problems that you're trying to address with the Runhouse tool chain?
Yeah. So
the first thing that we
took aim at with Runhouse is actually just the opinionation itself. So we asked a question like and I we're working at Meta. I had seen this rearchitecture
of the infrastructure
happen several times. Like, I was very intimately involved in the rearchitecture of our recommendations modeling stack to accommodate new AI assumptions or, let's say, the the assumptions that we had made before breaking and having to completely rearchitect.
So
the the first question was basically, how can we make it so that if somebody wants to use PyTorch distributed
or Ray or Horovod or whatever, like, whatever they choose to use, whatever methods, whatever arbitrary kind of combination of sequences in their program that they wanna do, how can we just remove all opinionation
and do it in a way that doesn't introduce a new layer? Like, rather rather, we should be removing layers. So
rather than sort of introducing a unified,
you know, next thing that everybody should migrate to, how can we make it so that whatever they however they work today, they can basically kind of tap in and use
arbitrary compute in whichever way they're most comfortable.
And so that's kind of the core tenet of the system. It's a an effortless computing platform for distributed AI machine learning from the perspective that wherever you run Python, and it's just totally arbitrary Python is where you can actually interface with your computer run house. Wherever you run Python, local IDE, local,
notebook,
or in production in a, you know, an airflow node or in a pod deployed in Kubernetes or whatever, it doesn't matter. Be able to interface with your, like, powerful platform compute, you know, in a in a platform as a runtime manner, not having to, like, deploy
to execute, but rather to actually just be able to traverse the powerful compute and utilize it within your program. Can we do that in a way that the the the compute itself is completely a blank slate, and just whatever system you wanna use on it, you can use on it? So
the way that we see it is sort of like Snowflake for ML, where
Snowflake
had this, like, really powerful kind of step function difference for, for an individual data engineer where maybe
the work that they have to do is, captured as, like, a SQL query of some kind. And previously, maybe they needed a team to manage a Hadoop cluster or something like that to be able to actually get this query running.
And with Snowflake, it's very effortless. You just take your SQL query, just throw it at your cluster, and it just runs it.
For us, in ML,
the the the analogy is just like arbitrary Python and arbitrary
distributed Python at that. Just being able to take that, throw it at your compute platform, and it'll just run it. And so we've architected Runhouse to basically do the same thing. So wherever you run wherever you run Python, you can dispatch these kinds of distributed Python
workloads at the system, and it will schedule and execute them on your own compute
using your own data, obviously.
And if you want to use Ray or you wanna use Dask or you wanna use PyTorch, you wanna use TensorFlow, it doesn't really matter the distributed framework. It will distribute
automatically distribute and execute your code. And it's extremely unopinionated in the sense that you don't have to migrate your own Python code to it. It doesn't give you a DSL that's, like, somewhat limited in what you can do. It's actually, like, aggressively DSL free. You just take whatever Python you have, you throw it at the platform, and it distributes and executes it. So you don't need to basically decide, okay. All of my code is not going to be Ray code. Right? We've adopted Ray as a unified platform, or all of my code is going to be, like, all of my ML stuff runs on SageMaker. So we have to just accommodate the way that SageMaker tells us the structure of training jobs. It's just completely arbitrary. You can't outgrow it. It's not possible. I've even run, like, Andrei Carpathi's, like, llama CPP
with the system, you know, deploying and recompiling with the each execution.
And then,
we were very inspired
by Ray's solving the kind of research and production fragmentation
and wanted to make sure that people were not dealing with translating from research to production ever with such a system. And so from the outset, it is, like, completely unified for both research and production. And that was, like, something we worked on for over a year, just making sure that the workflow of your iterating with the system is as responsive as, let's say, a notebook.
Meaning that when you're re executing some super powerful job that happens within your Python workflow,
it's going to redeploy and re, you know, and start executing your code within about a second. And then on the production side, it has to be, like, super fault tolerant and reproducible through these, like, super wide workflows, and that's kind of, like, just a square zero requirement for production teams. And so that's where we kind of solve a lot of these extra cluster problems. So where, you know, Ray or Dask or PyTorch distributed or whatever are super, super handy
on the cluster, they give you, like, very, very powerful
distributed
methods on the cluster. We wanna basically give you, solve the problems outside the cluster for you. So if a fault happens on the cluster, you could just catch it in Python. Or,
if you wanna have some really complex heterogeneous workflow that involves multiple different clusters and multiple different distributed
systems interacting with each other, maybe even multiple clouds. Like, we do that pretty natively.
That's completely supported just in Python through without having, like, a DAG DSL
or any kind of opinionation about the flow of execution.
And then the last piece that we focused on was basically being, like, a true platform as a runtime.
So by not having any kind of local runtime requirement or local kind of cluster structure,
we can be, like, extremely
unopinionate about where you execute from.
And one of the interesting challenges too about the ML space in particular is that it's building on so many other layers where you have to have the data available to be able to do the training. You have to have the data available to do the exploration and building the features.
You also need to have infrastructure
or some capacity to provide infrastructure to be able to execute on, so that often brings in some sort of an infrastructure or a DevOps team.
And I'm curious how you think about who the target user is for Runhouse
and some of the ways that you work to support collaboration with those other roles and responsibilities that all tie in together to actually building and training and deploying these ML systems.
Yeah. So a system that's sort of Snowflake for ML,
it has, you know, 2 core audiences. In the same way that Snowflake has your people who are just, like, throwing SQL at the system
and then people who are actually managing and configuring the system,
We also
tend to see
a split in enterprises or, among ML teams between the people who are just, like, throwing workloads and the people who are actually just keeping the lights on, making sure that those workloads execute.
But by being in this sort of Hadoop era of AIML,
usually what that means is that those teams are extremely coupled together,
and the infra team, the team that are that's actually managing the execution of the workloads, tends to be extremely overworked and very focused on unblocking
individual
jobs. And that's, like, a very common pattern. We see teams that,
basically,
their infrastructure
road map is actually just the specific
items on the road maps of their client teams that they need to unblock this quarter. That is just an anti pattern in my view.
So
we empower the
ML practitioners themselves, the people who are in, like, a research or an ML ML engineer role, to not really think about the infrastructure and specifically to not ever have to translate
the ML work that they do because they've been given a template or specific execution requirements
by the infrastructure team. So they're not going to be working in notebooks and then translating into airflow or
babysitting the translation
after they've done their research and handed it off to an infra team, babysitting that, like, this thing is being converted into the production stack in the right format,
they just get this extremely neat, extremely
flexible environment to be able to show up and do their ML work every day.
And when they want to grab something in production and make a tweak to it and
see what impact that has. That's just a normal experiment like a normal software engineer would do. They don't have to translate it back into the notebook environment or any crazy things like that. On the infra side,
what we want is basically to liberate those teams from actually doing the conversion or from actually having to do the
airflow debugging and babysitting that is really characteristic of those teams.
So
if you
see failures of some particular type of job in production,
you should be able to go and look at the way that it failed
and catch that exception if it's an exception that, you know, shouldn't be happening or needs to be handled in a particular way and be able to systematize
that knowledge within the system without having to figure out a way to, like, rearchitect your entire system, be able to handle that new type of fault or something like that. So for the for the infra teams,
unifying the computer state also means, like, liberating themselves
from a lot of the really, really gnarly work of ML infra, like, you know, repeatedly debugging these, like, really pernicious errors and finding ways within your opinionated systems to handle them, and also just being able to unblock scale much more natively. So
if maybe it was a job of an infra team to figure out how to get the platform to a place where this single GPU training can now happen multi GPU because of a company priority to scale such and such model or to go from,
you know, single node multi GPU to distributed
or to go from
TensorFlow distributed to PyTorch distributed because we need to take advantage of this open source library for this company priority.
It shouldn't be that that is a, you know, 6 to 12 month activity to be able to progress
machine learning proper within the company
up the sophistication scale. You should be liberated
to tell your
practitioners, use whatever systems you like, interleave them with whatever existing systems you're using,
and kind of introduce an opinionation in that matter. So it's really designed for both sides. The opinionation
benefits both. I would say that the teams who are
most aggressively reaching for the system are the ones that are basically staring down that challenge
today. They're like, our teams want to use you know, this team wants to use Ray for this one thing. Are we gonna have to rearchitect our entire stack so that now in Kubernetes, we're launching Ray clusters instead of, you know, regular pods, or we're in airflow.
Every time we get to a node, we can run Ray on it. Or all of our researchers have some sort of launcher that they can hit where they can
request a Ray cluster instead of, you know, whatever VMs or or or containers are requesting today.
And Runhous is extremely surgical and and, una un
obstructive or undisruptive for those situations because inside of whatever code you already have running, inside of whatever orchestrator you're already using or inside of whatever,
day to day dev process your ML researchers or engineers are following, you can now just take that block of code that you want to distribute with Ray and use around how to send it at your platform,
turn it into you know, automatically distribute it, wire up the Ray cluster, and you haven't had to lift a finger to be able to introduce Ray into your infrastructure mix, and you haven't had to migrate off of something else to do that, and you haven't had to migrate the code proper to adopt the Ray DSL anywhere other than that one place where you wanted to use it as a, you know, handy distributed library for that one thing, like hyperparameter optimization or, you know, data processing or something like that.
Digging into the implementation of Runhouse, can you talk through some of the ways that you approached these design objectives and the end user developer experience
and how that influenced the way that you architected the solution?
Yeah. So when we started Runhouse,
we we were already talking to 100 of ML teams and continued basically just working closely with the ML teams we have relationships with and new ML teams across startups and enterprise
and, you know, like, AI native companies
to arrive at what we felt was basically
rid a a system that was that was AI compute at your fingertips, but completely rid of
the opinionation that these teams might run into. And such a diverse set of teams, you run into a lot of opinionation.
The first one that we were very sure we needed to get rid of was
underlying hardware or the the choice of compute that you could use. So we didn't want to have to, like,
change the system
to be able to handle different kinds of of acceleration, like different kinds of GPUs as new GPUs are coming out, or even different types of CPUs or different types of operating systems,
or the
even, like, opinionation about the specific,
resources that you allocate within a cluster,
like being able to be hyper efficient about the fact that this particular job needs, you know, just this amount of disk and just one GPU or something like that. We didn't want any of that those restrictions.
And, the best way to do that was just to allow people access to whatever compute they're already accustomed to having access to, which meant their existing Kubernetes clusters, their existing cloud accounts.
And,
and and and that also solves this, like, you know, major problem of not having to deal with or not having to introduce entirely
new security and configuration
and management and all that kind of stuff. Whatever you already have in place, if we could basically run on that compute, then we're not introducing a new ML silo or actually
destroying a silo and just bringing all of your existing compute into one estate as it were. So,
that was a, you know, hard decision we made at the outset was we're not going to repackage the compute. We're going to be use use people's existing computing data as aggressively as possible. And then what it means to actually provide a platform as a runtime or a compute foundation in front of that means to basically give all the ML researchers and engineers
a place where they can actually request the compute live in code,
similar to, you know, requesting a Databricks spark cluster inside your code. You're making a request to Databricks' own control plane, and then they're launching the compute however, you know, they're configured by your admins to actually launch it. And we do something similar just without the opinionation that has to be a spark cluster. It's just an arbitrary cluster.
So the actual architecture run house is you have a client of some kind, and that runs in Python with no local runtime whatsoever. So arbitrary Python can run it, and it can dispatch arbitrary Python workloads.
And then
you have the control plane itself, which we have, like, a hosted control plane that we just we, you know, run inside of our own cloud account, or you can deploy the control plane inside of your own cloud. But in either case, it's basically just an API front end that's getting a request for a cluster
and then getting you back the cluster to your local client to then be able to natively work with. And then
that control plane is basically allocating the compute from wherever you've configured it to have configured it to have access to compute. So it can have, you know, Kubernetes clusters that it can pull pods out of. It can have cloud accounts that it's able to allocate VMs out of, and then you can control where and how the work to be done and the cluster to do it on are kind of matched in terms of the actual prioritization
and permissions and queuing and those kinds of things.
And from the infrastructure side, side, I know that teams will sometimes have requirements of, you know, we want to limit this type of spend or we can only use these types of instances
or, you know, maybe there's some complex scheduling that needs to happen as far as access to GPUs, which are perennially limited in terms of their availability.
And I'm curious how you think about some of those requirements of being able to manage those constraints from the policy side that exists within the company while still preventing any sort of bottleneck on the ML teams.
Yeah. So
the the configuration itself, actually,
it doesn't need to be this, like, extremely
complex
scheduler
and prioritization,
like, kind of slurp 2.0 or something like that, Because,
ultimately,
the
if if you request compute as a user
and you're requesting it not in the form of a job to be submitted to a system, but rather compute to be given back to you, it actually really inverts the structure of what can be, you know, what what actually
you can expect to happen
and what what edge cases need to be accommodated. So if you're,
you know, an Argo pipeline or a researcher that's requesting
n number of GPUs with this amount of disk right now,
the 3 things that can happen if that compute isn't available
are, 1, you can get an error immediately that says compute is not available. Deal with it. 2, you can get basically queued and
then just use, like, you know, normal Python async to be able to handle that behavior, do other things in the meantime, or just, like, wait. Or, 3, we can launch fresh compute.
And
because it's the user's decision to essentially do what they will about that information,
you can
limit the things that need to happen on the scheduling side much more aggressively
because you can you know, it's now kind of the user's own decision if they want to
wait indefinitely
or fail and raise allowed error or something like that. Whereas if we were, like, an orchestrator,
for example,
then we would have to have all kinds of support for handling every possible thing that can go wrong and notifying you for each of them and then, like, making sure that we're not too noisy if you're actually not supposed to have compute here and you're supposed to be waiting and all that kind of stuff. So, you know, that that's one one piece of this is just is that the configuration itself can be relatively narrow. You need to be able to queue if you want to. You need to be able to tell a user they don't have quota to launch a particular type of compute. You need to give the user the control to be able to
request compute in terms of, like, logical units. Like, I need, you know, 1 a 100 or I need a a 500 gig of disk or physical units. Like, I need
a g 5 xlarge or whatever. You know, that that that level of control is important. But other than that, you don't need a ton more. And the fact that you can that that Runhos is really designed to be a unified interface compute
also is we've intentionally designed it so you can you you can and should use it from within a system that is good at this type of stuff. So you should be using Runhouse.
If you love
Prefect or Airflow or whatever because it gives you really good, you know, retry logic and fault tolerance or because you love its you you know, you love the caching or you love the way that it notifies you about jobs. It represents dependencies between jobs. Keep on using them. Right? And then just use Runhouse inside of that orchestration
to actually access your compute on the fly.
And that also, you know, makes it so that we don't have to completely
rebuild the world of orchestration to handle all these different types of faults and different types of scheduling. It significantly
simplifies our contract from the user's perspective. You use Runhouse to take some really heavy
or, what do you know, some machine learning activity
or Python activity for that matter that you couldn't do in the local environment, either because the data's too big or because the compute is too big,
and then make that thing run you know, request the compute that you need to be able to run just that one thing
and, you know, handle the case where the compute isn't available,
and then that's it. You know, that that's that is your entire workflow, and you can do that
in an extremely ways
from within your workflow. So you can mix many clouds within the same workflow or, you know, do multiple things with the same cluster for multiple stages of workflow in order to save costs and things like that.
And given that all of that infrastructure management is in the control of the person who is building with Runhouse, I imagine that any aspect of data locality to the compute that you're deploying is something that is in the control of that person as well, where Runhouse is agnostic to any of that evaluation of saying, oh, I want to run this job. It needs this data. Let me make sure I'm running in the right AWS region, etcetera.
That's just that the user understands what their operating environment looks like, so they will decide, oh, I need this data, so I'm going to launch this type of cluster.
Yeah. So we we've actually we've heard that
the requests to provide more automation there from a few companies.
I think that our attitude about this is that
the first step is just to give the user the control.
Like, today, you don't really have the control. I can't tell you how many companies we've spoken to where they have, like, multiple Kubernetes clusters in different clouds or in different regions,
And the job of the ML engineer researcher
is to actually, like, go into some UI in the morning and look at where the compute is available.
And if the compute isn't available inside of the region where their data residency requires that their data has to live, then, like, it's a ticket to the infrastructure team. So
just the fact that you have relatively low level control
within your job that you want to run this stage of your workflow on, you know, inside of GCP and then this stage of your workflow inside of AWS, and you're not doing this through, essentially, like, replicating the user interface to the compute and replicating the entire control plane in each of the places where you have the compute, so it's very much a user activity to make these, like, really course decisions at the outset,
is a major problem solve for a lot of people.
We've spoke to companies where they just can't
progress past
CPU only gradient boosted decision trees for for one company, it was like, they have a $1,000,000,000 run rate business that depends on CPU
gradient boosted decision trees because the data residency requirements make it such that they need to train
models on e inside of each of the separate regions where they're gonna be utilizing different, you know, countries' data.
And therefore, like, they just that's that's just too complex for them to handle. They just don't have the you know, they can't replicate the control plane, and they only their control plane is built around Kubernetes and just, like, not fundamentally multi cluster. Just giving them the control to be able to take the same ML pipeline
and then just have it, you know, run this thing on this region, run this thing on this region, run this thing on this region is already a major step forward. Now
I think that there's another
interesting problem to be solved, which is to say,
in a declarative manner, the same way that I declare I need
x GPU,
that I need x data string or something like that, and then the system is also going to allocate a customer for you based on where it know what what cloud it knows that that data is in. But it actually just bridges us into an entirely new territory, which is essentially like data cataloging.
So I think that our opportunity there is quite interesting, which is that
if you're somebody that has a very rich governance
structure built around your data, it would be better for you to be able to bring the ML workloads into that platform, into the data walled garden,
than for you to have an entirely new ML super optimizer that is, like, integrated with all of your data and hyperdata aware or something like that. So, actually, this is something that we've that we've thought about, like, basically building native apps on top of Snowflake or Databricks
where basically just we can complete that sort of embarrassing
crashing out moment for the ML teams out of the data platform. So the lineage and the governance and the odd, all that stuff just carries straight through. And because it's a native app, the compute gets allocated
by the data platform in a way that is already aware of the residency requirements the same way that your Snowflake clusters are aware of where the data lives that they're gonna be processing or your Databricks Spark clusters
are aware of the data that they're gonna be streaming in based on, you know, the string IDs on the Databricks platform.
And as you have been building Runhouse, working with end users, customers, community members, what are some of the most interesting or innovative or unexpected ways that you've seen it applied?
Yeah.
So I think
one of the areas that I've been
particularly surprised is on the fault tolerance side. I think that fault tolerance is something that we tend to see as sort of 1 dimensional.
Just like errors happen, like, what do we do about them? Like, retry,
you know,
notify the user, and then kind of, like,
maybe cache and restart the entire job. But when you have the full expressivity of Python
to be able to handle faults in super wide, super heterogeneous workflows,
then you tend to see actually a lot of creativity in how those faults are handled. And, actually, the benefits tend to carry
much further than just, like, better automation and lower fault rates. So, we have one team that uses Runhouse for, like,
recommendation
system service that,
requires the retraining of a couple hundred, or
it it requires basically, like, a few thousand,
training jobs to be executed through Run House per week. And, you know, when we started working with them, they were doing this remote execution via SageMaker training.
It was, like, extremely
human demanding from a fault tolerance perspective. They spent
50% of their overall team time just, like, debugging errors that happened and then trying to, like, manually short circuit them.
And so and, you know, the fact that you can't SSH into a SageMaker cluster, the the overall debugability and accessibility of the system is poor enough that, like, that actually just makes sense from, like, a team time perspective. But, also, just from the perspective that all SageMaker training will allow you to do is sort of a Slurm like
execution where you submit a CLI command. And if that CLI command fails, what are you gonna do about it? Right? You're gonna parse the logs to see that it was an out of memory error or something like that? You you just can't do that. Right? There's a hard line between the remote thing that's running and the local thing that's running. So
because Runhouse doesn't have that hard line, right, it's just you send off your Python to the remote compute,
and you get back a callable that's identical to your original,
your original function or your class that you sent off to the that you dispatch to the remote compute. Now you don't have, this you have, like, very, very rich control over what you actually do with that from a fault handling perspective. So what we saw that team do actually was that they
launch all of their trainings
on, like, relatively small compute up front, and then maybe they know that, like, 10 to 20% of those are going to Oom. And before,
those ooms were just, like, purely
wasted team time. They couldn't onboard new customers because they were just, like, investigating and short circuiting these ooms constantly. With the Runhouse, they just catch the oom as, like, a regular Python exception
and then just, like, destroy the cluster and then bring up a new bigger cluster to, you know, rerun the job for that 10 to 20%.
And so, overall, their their their failure rate for their pipelines just, like, plummeted. It was, like, 40% before we started working with them, and it's, like, less than a half a percent after. And those are all, like, very easily explained errors that are just aren't such a priority to fix in such a complex way. But so for 1, they were able to operationalize the debugging and incorporating the system that just destroys the fault the the the failure rate. But then secondly, they're able to save a ton of money because they were able to launch all their jobs in a way that they were expecting ooms from some of them, and it's just not a big deal. Right? Like, you catch an OOM, destroy the cluster, launch a bigger cluster. They do that actually multiple times. They'll destroy the they'll they'll catch another room, destroy it, bring up a new one. You could do that with many, many kinds of faults. You know, if you get some sort of node failure in training, you don't have to tear down the entire cluster. You don't have to nuke all the data that you've loaded into the file system. You can just, like, calmly
kill the training job,
trigger a new training job that rewires up PyTorch distributed so that your failed node or your failed process or whatever is now brought back into the mix or just completely nuked out of the cluster. It's totally fine. You can make sure to checkpoint if you have a failure. You can do checkpointing in parallel,
multithreaded because it's just trivial. It's just a separate call to the cluster. So that type of, like, really sophisticated
interaction with the cluster from a fault or from a resource utilization perspective,
I was expecting that to be, like, a super user thing that nobody would really do. People just do it instantly because it's just Python. Right? It doesn't really require much more brainpower to know how to catch the exception.
Yeah. That's definitely very cool.
And in your experience of building this system, investing your time and energy on RunHouse, what are some of the most interesting or unexpected or challenging lessons that you've learned in the process?
So I think that one of the hardest things is
operating
relative
to the kind of grain of,
hype cycle.
You know, you can't really even if you know
that you have a sort of technically excellent
correct solution,
and, you know, you've talked to hundreds of companies, you know exactly how it's going to fit. You've tried to you made everything, like, extremely low lift. Ultimately, you know, it's 2022,
and
the thing that many people are asking themselves is not, how do I, you know, fix the ML development and fault tolerance and
efficiency and compute utilization story,
it's like, you know, how do I incorporate LLMs into my workflows, or how do I, like, deploy a chat app? You actually, you know
meeting people where they are is, like especially in, like, large enterprise is super
important. And I think that patience
that people are eventually going to get to the section of the curve where they are hitting these problems that you know that they're kind of barreling towards is really important. Like, if you just try to kind of go out screaming
and raise this sort of, like, doomsday story that everybody is, like, building these super unscalable stacks, and they're gonna end up hitting all these error, you know, issues that that you saw inside of, like, Meta or, you know, Airbnb or Netflix or whatever, like, that alarmism doesn't really get you anywhere.
But finding the people who are, like, the early movers and are really,
thoughtful and aware of how their systems are going to scale
operationally
is, like, probably the most important thing. And, you know, gradually, what we've seen is that
those problems,
you know, you have to kind of trust that those problems are going to eventually show their faces.
And especially now that I think there's more and more of a recognition that and the essence of ML, like, the magic of ML that has been,
you know,
put into,
that that that has yielded, like, you know, transformative
business results for Google and Facebook and
Netflix, etcetera, etcetera,
is actually mobilizing first party data. Now we're seeing that actually, like, training and incorporating first party data is now completely back into the fold, and people are raising this question of, like, how do we do this with extremely heterogeneous,
you know, AI methods and extremely,
an extreme diversity of sophistication
in the different place in in in how we're actually utilizing compute or,
distributed frameworks inside of our organization. Now those are kinda coming back into the fold.
I think that there's a really dangerous kind of moment right now to be avoided, which is a lot of people kind of running around trying to convince
others that
you will not need to train models anymore. You could do
all the ML stuff you previously did strictly through calls to some sort of hosted ML system, which are kind of playing to
the challenge of ML itself. Right? They're pointing out the fact that or they're they're they're, taking advantage of the fact that ML itself is hard and unfamiliar for a lot of people and trying to get people a plausible easy way out that maybe they just won't have to invest in it or do it. But in practice,
you know, that's a really dangerous message because then you're gonna have very, very extreme centralization,
work or,
concentration
of, expertise to mobilize first party data among very few people.
And the, you know, the the I guess the precedent for this would be, like, the ad ecosystem. Right? Like, if you only have a very small number of players
who invest way, way more than everybody else does in their ad systems, right, their ability to mobilize first party data
to,
buy or auction ads super, super effectively, then everybody else just needs to kind of consume those ad systems through the few. And in ML, I think that the more we aggressively
democratize
ML by making it so that you can just, you know,
whoever you are inside of a company, whatever type of engineer you are or whatever
org you're in, you can tap into and take advantage of the hard parts of ML infrastructure, like, you know, distributed frameworks and compute.
And we try to democratize
the a obviously, open source is a huge part of the story. We democratize the methods proper. And so now you have 2 of the most important ingredients. You have the the kind of prior art of what's actually worked for somebody else in the form of the open source AI methods, and you have the infrastructure at your fingertips, you don't have to do a math massive lift to be able to unblock this within your organization.
The only remaining piece is the first party data. Right? And that that actually, I think,
is a really important message for us to be, you know,
bringing out to these enterprises in a way that doesn't kind of,
scream
this sort of, like, doomsday scenario at them that, like,
they're, you know, that that that their ML stack is about to get nuked or, like, you know, that the GPUs are gonna go to GPUs are,
gonna become significantly cheaper and you need to accommodate your you know, accommodate greater diversity in GPU stack or something like that. Just, like, bring them those basic ingredients to do ML magic, and they'll wanna do it. And then, you know, we'll avoid this sort of scenario where only very, very few people actually have the expertise to mobilize their own first party data.
And for people who are building ML systems, they're doing ML training, they want to improve their overall workflow, what are the cases where Runhouse is the wrong choice?
So
one of them is just the AI Lab use case. Right? If you're training a low cardinality of models,
right, you're let let's say you're,
you're training a model specifically to put it on hugging face and gain some brand recognition, you don't need Runhaz to do that. Right? Just you you can use you you can set up the infrastructure manually. You don't need so much automation. You can SSH into it if you, you know, just spin it up as VMs or whatever.
And, you know, weights and biases is really good for observing your training workflows. Like, the ML
research that sort of looks like what you would do in grad school workflow, I think, just doesn't really require Runhouse. Runhouse specifically shines when you have,
somewhat heterogeneous compute. Right? Maybe like multi cloud. We really excel at multi cloud because the decoupled structure of the client that's running the thing and where the thing is run makes us really agnostic to the choice of, like, hybrid, you know, on prem versus cloud versus multiple cloud accounts, whatever.
If you don't have your heterogeneous hardware, if you don't have so much,
recurring
execution where you need fault tolerance or automation or you don't have such wide workflows,
then, you know, Runhouse just you don't need that level of sophistication.
I I think that the,
the case where actually you might be doing some really homogeneous
activities,
but where Runhouse still excels is the collaboration use case. Because I think that the ML infrastructure
or maybe even just, like, infrastructure broadly
has not been built for a sort of multiplayer
user journey
in the way that we we've basically focused focused on this from the beginning. And even if you have, like, you know, one cluster or a Kubernetes cluster that you're launching a few containerized things out of,
the collaboration benefit of just being able to, like, share a cluster that you're working on with a peer, send them over your notebook or your script, and have them just be able to pick it up and run it as just like normal code utilizing your cluster is pretty magic. That's one where I think we do deliver a benefit, but, you know, we're you're you're not really doing
mobilization of first party data in a way that's kind of complicated or painful. You just don't have that much infrastructure overhead, and your stack Runhouse is not exactly
solving that problem.
And as you continue to build and iterate on Runhouse, what are some of the things that you have planned for the near to medium term or any particular aspects of your road map that you're particularly excited about?
Yeah. So I think that the the the data estate
finally being unified with ML activities is something that I'm really excited about. I think that it just shouldn't be the case that,
you know, the NL people are looking enviously at the data side where they could just, you know, ship off a SQL query or load up a spark cluster and know that everything is authenticated and all the lineages tracked and everything like that. So
being able to, like, integrate really, really cleanly into that and make it so that the activities you do on your clusters via Runhouse
feels like it's within this, like, unified platform experience is something I'm excited about. How exactly that actually flows, I think, is still kind of like product and user journey question.
And then just greater and greater control
on the part of the admins,
I think, is something that we will continue to invest in for a long time. Just, you know, richer
preconfiguration
of your clusters.
And we support arbitrary Docker containers and lots of kind of configuration activities in code today. So you can set up the cluster however you want to. And if you see someone else setting up a cluster, you can exactly
copy and paste your code to pip install the same packages or whatever. But I think that the preconfiguration
of compute
so that it's really, really seamless how the compute gets delivered to the ML engineers and researchers, and they get this, like, kind of magic,
I don't think about it at all. I just ship off my thing,
and it will run-in this on my compute platform. Those are things that we're excited about.
The last thing I would say that we'll just continue to invest in is, like, making sure that we can support arbitrary ML like, continue to support arbitrary ML methods going forward. So I think we have, like, very good coverage today. We've made it so that your choice of distributed framework or your choice of
GPU vendor
or, you know, cloud provider is not really a factor. But the methods, you know, constantly move. And even if, let's say, people are eventually, like, eventually, like, move away from Python or start doing
ML and,
Rust or JavaScript or something like that, like, those are decisions that we thought about. We're not really we don't have a good reason to do them today, but, like, just continuing to make the system more and more future proof is something that we're excited about.
And as you keep an eye on the overall space of ML infrastructure, ML workflows, the role that generative AI and AI more broadly is playing, what What are some of the future trends that either you're seeing come to fruition or predictions or desires that you have for kind of that future world of ML and AI and developer experience across them?
Yeah.
So I think that some of the best ML teams that I've
encountered
are ones that have a real systems focus about the ML.
They're not thinking of each
ML project as kind of a thing to be unblocked, where, like, they do some work inside of a notebook, and that produces a model, and then they put the model,
you know, into some sort of serving system and then, like, problem solved, or they turn that notebook into,
an airflow pipeline, and then they just, like, schedule a daily problem solve. They're actually thinking about the system as something that needs to be refined and iterated over time because
the nature of ML is that you it's it's very much an experimental
line of work. You
change you know, you you do 10 experiments. You tweak
your system,
in in a way you know, in some minor way 10 times, and 9 of those times nothing happens or something bad happens. And one of those times, it's actually
delivering
transformative results that you couldn't
get any other way, you know, like top line kind of business
impact results,
then, you know, those are the teams that we see actually with this, like, hyper focus on the research for production time, hyper focus on the ability to, like, you know, experiment same day on production data or production scale compute, not give, like, toy compute inside of a notebook environment or something like that or toy data inside of some isolated environment. So
I think that as more and more teams actually recognize
that ML is distinct from traditional development in that way, that, like, you're actually
trying to create as much democratization
among your workflows to be able to try anything on the system as you can and do it at scale, then more and more people are going to end up hitting a transformative impact, you know, and and having maybe more of a realistic view about
why
increasing the throughput of experiments equals dollars versus just, like, making it so that the ML team has
3 top line priorities as an org to unblock these specific
ML wins that we expect to deliver impact, but, like, aren't exactly sure. So I I think that in ML, in general, you make a lot more money from the places you don't expect than the places you do. And then not having to having those wins actually in the bag so that you don't have to constantly rejustify that upwards is going to be something that will fundamentally change the ecosystem, I think, because,
you know, it it will it will stop having to be this, like, back and forth between the CFO and the ML team about what exactly they're doing, which is obviously very different from, let's say, the way that a CFO talks to, like, a front end team.
But I think that the Gen AI
detour has sort
of taken us aside of the like, you know, taken us away from that for a brief period of time, and we're gradually coming back. And so the sooner we get back to that, I think the sooner most of the magic that ML delivers is going to happen again, and people are going to continue trying to march up the sophistication ladder of the ML system that they already have and democratize their ML infrastructure for more and more people to be able to, like, try out you know, try things out in in weird, unexpected places inside the company.
But if everybody's like, the most important thing for us to do via in our ML organization is, like, build a chat app, then probably less of that magic is gonna happen.
Are there any other aspects of the work that you're doing at Run House or the overall space of ML infrastructure that we didn't yet that you'd like to cover before we close out the show?
Yeah.
So one one area that I think is actually
not really so discussed
in the, like, in the discussion of, like, architecting ML infrastructure
is how
the,
the ML infrastructure organization
relates to existing
DevOps, like platform engineering organizations or data infrastructure organizations.
And that, I think, is an area that we've largely shied away from. 1, because
the MLOps wave
really tried to convince everyone that ML was special, and it required special systems, systems that you should buy from me. You know, that was sort of the the general vibe. And, also,
the organizations themselves are very heterogeneous. Right? Like, it wasn't that there was a particular structure that you could point to in enterprise and say, this is how your team should be organized, and you should have your, you know, platform people that, you know, maintain your main platform and your ML platform people and your data platform people. Like, it was just completely all over the place, and I think that Gen AI and continued reorgs in AI data organizations is actually kind of worse than that.
But I think that this is, you know, why we focus really heavily on being precise about what is different about ML development from, let's say, like, web development or from data engineering. Because the more precise you can be, the more you can utilize your existing expertise within your DevOps teams, your platform engineering teams, and not fork the stack
and essentially
gatekeep it from the rest of the organization where it's like we're using ML specific tooling that only the ML engineers understand and the ML researchers understand, and only the platform team really,
has the expertise to manage or even, like, just from a compute efficiency perspective, like, we have to keep that compute totally separate from our main compute that we utilize. So I think this is actually a really, hopefully,
a positive trend that we're seeing that platform engineering teams and traditional dev ops teams are getting more and more involved, and ML teams are asking the question more aggressively,
can we not utilize
ML tooling? Right? Like, ML op stack and just utilize, like, very, very well understood tooling or tooling that's really well understood within our organization, like Kubernetes
and Argo
and Knative or, you know, whatever. The the much more industry standard platform
tooling.
And then say, how does this need to be tweaked so our ML people could be productive with it and not, you know, be able to accommodate
the fact that there's no local development, you know, and these systems are built under the assumption that you could develop locally and then containerize and deploy. So that's not available. Right? If you need to be able to run on the platform, can we just tweak to be able to, to to accommodate that difference? So I think that, like, defragmentation
or kind of layering of platform engineering organizations
is, like, a a trend that we're
seeing, like, glimmers of and looking forward to expanding so that we have less, like, score shirts from people very dramatically adopting some big ML ops system that's promising to, you know, solve ML in a in a perfect and final way by giving them exactly the workflow that they should be following from ML. People kind of go back to just like standard code testing,
deployment, CICD best practices that they already understand their organizations.
Alright. Well, for anybody who wants to get in touch with you and follow along with the work that you and the rest of the Runhouse team are doing. I'll have you add your preferred contact information to the show notes. And as the final question, I'd like to get your perspective on what you see as being the biggest gaps in the tooling technology or human training for ML and AI systems today.
I I think that
everybody
saw the
democratization
of AI
at the same moment
via
the hosted API workflow.
Right? Like, being able to call into a massive LLM just by an API endpoint was kind of magic in the same way that, like,
utilizing, you know, automating text messages through Twilio is kind of magic compared to, like, however you set up kind of the, like,
phone
based interactions with users before.
And I think that we should be extrapolating that much further.
Right? So,
obviously, the more you, you know, put an API in front of something, the more opinionated it's going to be. But just the concept of shared services at people's fingertips, both from a cost efficiency perspective, so you can, like, much more aggressively bin pack, and from an accessibility perspective, I think, is a good thing. So, you know, in many organizations before
the Gen AI hype cycle,
there was no shared text embedding that you could call. Right? You know, BERT obviously existed.
The the concept of taking embeddings over text was extremely common. But, like, in a typical organization,
you couldn't take, like, a text embedding or a,
an image embedding through a ResNet or whatever that might be. And then, you know, following the Jet AI hype cycle, now those things are much more standard. And I think we should be going a lot more further with that. Not being prescriptive about what our
engineers should be having access to from, like, a scaled
AI perspective, but more of, like, buy us for us type stuff. So if we have 10 pipelines and 10 researchers who are who are running, like, basically the exact same batch evaluation
because that's, like, our standard evaluation gauntlet. Like, we should serviceify that and put it at their fingertips and binpack it on, you know, binpack it onto one cluster rather than just having all of them allocating separate compute for
that job and, you know, having to solve it from scratch and having, like, you know, a 1000000 different copies of this particular way of doing something. I think more aggressive sharing of subsystems within ML will be a good thing. And I think moving away from, like, the definition of done for production of ML being a a a DAG, an ML pipeline DAG is going to help with that because when your definition of done is a pipeline DAG, you end up with just a large collection of ML pipeline DAGs that don't really share anything between them because they have to be isolated by definition.
So serviceification,
I think, as, like, something that
as a direction for the future of ML infrastructure and developer experience, something we're also pretty excited
about. Thank you very much for taking the time today to join me and share your experience and expertise in the space of ML infrastructure
and the workflows that ML teams are relying on, the bottlenecks that they're hitting, and for the work that you're doing on Run House to help alleviate some of those challenges. So I appreciate all the time and energy that you're putting into that, and I hope you enjoy the rest of your day.
Great. Thanks a lot for having me.
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