Effortless model deployment with MLflow

Save your Machine Learning models in an open-source format with MLFlow to unlock effortless deployment experience later.

Welcome back to the series Effortless model deployment with MLFlow!

1. MLflow: Introduction to the MLModel specification (this post).
2. Customizing inference with MLflow.
3. Packaging models with multiple pieces: deploying a recommender system.
4. Packaging models with multiple assets: deploying a HuggingFace NLP model for classification.
5. Packaging stratified models (many models): deploying a partitioned model for demand forecasting.

MLflow is an open-source platform, designed to manage the complete machine learning lifecycle. As it is open-source, it can be used when training models on different platforms which allows you to avoid vendor lock-ins and to move freely from one platform to another one.

Most of the time, people are familiar with MLFlow’s capabilities for tracking experiments, logging parameters, metrics, and artifacts from a given run. It also comes with a nice user interface to compare and evaluate models.