Fireside Chat #7: How to Build an Enterprise Machine Learning Platform from Scratch

Russell Brooks is Principal Machine Learning Engineer at Realtor.com and has worked extensively as a software engineer, data scientist, platform engineer, and machine learning manager. In this fireside chat, Russell joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to discuss what building an enterprise ML platform from scratch looks like in practice, including the journeys he experienced at both OpCity and Realtor.com, where he took both organizations from a bus factor of 1 to reproducible and automated ML-powered software. After attending, you’ll know about - The ins and outs of what building an enterprise ML platform from scratch looks like in practice; What questions are key to answer when building an enterprise ML platform from scratch; - How to demonstrate the impact of the data and machine learning functions in organizations when doing so; - The most impactful ways of collaborating for SWEs, data scientists, platform engineers, and ML engineers, And much more! The fireside chat will be followed by an AMA with Russell and Hugo at slack.outerbounds.co. Find out more about how we think about MLOps, OSS, and human-centric data science tools here: https://outerbounds.com/ 00:00 Prelude 04:27 The fireside chat begins! 06:48 The path to data science and machine learning engineering 10:10 The value of ML in real estate and beyond 13:30 Demonstrating the value and impact of data science and ML in your organization 15:55 Building an entire ML platform from scratch 20:12 What is Metaflow and how does it help your ML function? 23:26 What exactly is production machine learning? 35:41 Staying on top of all the ML tooling: how?! 42:56 The moving parts of full stack machine learning 47:25 Data scientists, ML engineers, and platform engineers: how they work together (and how they don't) 49:25 Software engineering skills for data scientists