Fireside chat #2: MadeWithML.com -- Teaching Practical Machine Learning

Goku Mohandas, founder of Made with ML, has worked on machine learning and product at a large company (Apple), a startup in the oncology space (Ciitizen), and has run his own startup in the rideshare space (HotSpot). In this fireside chat with Outerbounds’ Hugo Bowne-Anderson, Goku will talk about the path from laptop data science to putting machine learning in production, for both organizations and individual data scientists. The modern capabilities of data science and machine learning are wonderful but, as an industry, we’re still figuring out how all the moving parts work together and what patterns we need to start repeating. In this conversation, Goku and Hugo will dive into the challenges of machine learning in production, what you need to know in order to actually deliver value with ML in prod, and what we can learn from organizations that have done it well, including Fortune 500 companies. After attending, you’ll know * How to get started today with ML in production: the tools, workflows, and mental models you need; * What ML in production looks like across a range of verticals, including Fortune 500 companies; * What steps your organization can take in order to quantify and minimize risk when adopting a machine learning strategy. The fireside chat will be followed by an AMA with Goku and Hugo at slack.outerbounds.co. 00:00 Prelude 03:15 The fireside chat begins 04:42 Introducing Goku and MadeWithML.com 14:10 The importance of continuous learning in ML and data science 18:55 How to teach (and learn!) machine learning in production 24:45 Learning production ML by working on projects 35:40 What ML looks like in Fortune 500 companies 43:40 The "bus number" definition of production ML 46:20 Moving from laptop data science to production machine learning 50:00 Test your code, your data, and your models! 58:35 Dependency hell 1:08:00 Build machine learning systems intentionally Find out more about how we think about MLOps, OSS, and human-centric data science t