Machine Learning is a very hyped topic of the moment. While a lot of the talks and presentations cover the data science component, very few cover the nity gritty details of a machine learning pipeline. This talk will focus on the engineering part of Machine Learning by covering different Machine Learning systems architecture best practices, strategies including design.
Machine Learning is a very hyped topic of the moment. While a lot of the talks and presentations cover the data science component, very few cover the nity gritty details of a machine learning pipeline. This talk will focus on the engineering part of Machine Learning by covering different Machine Learning systems architecture best practices, strategies including design. We will delve into the essence of Uber's Michelangelo, Airbnbs s Bighead and Facebooks FB Learner. During the talk, I will use MLFlow and Python as platforms to create an open-source based solution similar to the ones from the big tech companies for the everyday tech startup. The entirety of the cycle of training, deployment, monitoring, champion/challenger testing, and serving layer will be addressed. Technical debt prevention is another topic that will be addressed in the end of the talk.
The outline of the talk is the following: 1. Intro 2. ML Engineering concepts 3. ML Engineering Platforms : Uber's Michelangelo 4. ML Engineering Platforms : Airbnb's Big Head 5. ML Engineering Platforms : Facebooks FbLearner 6. DIY - ML Platform 7. DIY - ML Platform - ML Flow 8. DIY - ML Platform - ML Flow - Data layer, Training layer, Serving Layer 9. DIY - ML Platform - ML Technical debt management ( problems+mitigation strategy) 10. DIY - ML Platform - Demo