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In this, the kickoff episode of our AI Platforms series, we're joined by Aditya Kalro, Engineering Manager at Facebook, to discuss their internal machine learning platform FBLearner Flow.
Introduced in May of 2016, FBLearner Flow is the workflow management platform at the heart of the Facebook ML engineering ecosystem. In our conversation, Aditya and I discuss the history and development of the platform, as well as its functionality and its evolution from an initial focus on model training to supporting the entire ML lifecycle at Facebook. Aditya also walks us through the data science tech stack at Facebook, and shares his advice for supporting ML development at scale.
As many of you know, part of my work involves understanding the way large companies are adopting machine learning, deep learning and AI. While it's still fairly early in the game, we're at a really interesting time for many companies. With the first wave of ML projects at early adopter enterprises starting to mature, many of them are asking themselves how can they scale up their ML efforts to support more projects and teams.
Part of the answer to successfully scaling ML is supporting data scientists and machine learning engineers with modern processes, tooling and platforms. Now, if you've been following me or the podcast for a while, you know that this is one of the topics I really like to geek out on.
Well, I'm excited to announce that we'll be exploring this topic in depth here on the podcast over the next several weeks. You'll hear from folks building and supporting ML platforms at a host of different companies. We'll be digging deep into the technologies they're deploying to accelerate data science and ML development in their companies, the challenges they're facing, what they're excited about, and more.
In addition, as part of this effort, I'm publishing a series of eBooks on this topic. The first of them takes a bottoms-up look at AI platforms and is focused on the open source Kubernetes platform which is used to deliver scalable ML and infrastructure at OpenAI, Booking.com, Matroid and many more companies. It'll be available soon on the TWIML web site, and will be followed shortly thereafter by the second book in the series which looks at scaling data science and ML engineering from the top down, exploring the internal platforms companies Facebook, Uber, and Google have built, the process disciplines that they embody, and what enterprises can learn from them.
If this is a topic you're interested in, I'd encourage you to visit twimlai.com/aiplatforms and sign up to be notified as soon as these books are published.