Organizing the Machine Learning Lifecycle with MLflow
summary
Discusses the gaps between standard software-development tooling (issue trackers, VCS, CI) and the needs of machine-learning projects, and explains how MLflow can manage experiments, trained models and project setups across platforms and libraries. Demonstrates using MLflow to gain control over the ML lifecycle with a focus on running MLflow in a JVM environment and integrating it with JVM-based machine-learning libraries.
AI-written summary · about
chapters (4)
related talks
30mMLflow: an Open Source Tool for Managing the ML Project Lifecycle
57mManaging Machine Learning Lifecycle with MLflow
31mMLflow: Platform for Complete Machine Learning Lifecycle
33mOperationalizing Model Lifecycle with MLFlow
37mPlatform for Complete Machine Learning Lifecycle
45mMachine Learning Lifecycle Made Easy with MLflow
03mInterview
43mMachine Learning Engineering principles with Python and MLFlow