The ML Engineer’s life has become significantly easier over the past few years, but ML projects are still too tedious and complex. Feature stores have recently emerged as an important product category within the MLOps ecosystem. They solve part of the data problem for ML by automating feature processing and serving.
But feature stores are not enough. What data teams need is a platform that automates the complete lifecycle of ML features. This platform must provide integrations with the modern DevOps and data ecosystems, including the Modern Data Stack. It should provide excellent support for advanced use cases like Recommender Systems and Fraud. And it should automate the data feedback loop, abstracting away tasks like data logging and training dataset generation. In this talk, Mike will cover his vision for the evolution of the feature store into this complete feature platform for ML.