What is MLflow?
MLfLow is an open-source platform that streamlines the machine learning lifecycle with its four core components- MLflow Tracking, MLflow Models, MLflow Projects, and Model Registry. MLflow supports machine learning development with experiment tracking, reproducible runs through a packaged code, and sharing and deployment of models. It helps data scientists in building and deploying ML models by:
- Providing insight into how each parameter and hyperparameter influence a model
- Performing experiments while ensuring that the model is evolving continuously
- Enabling seamless model serving across numerous environments
How does MLflow help?
Enterprises generally face four significant challenges in their ML initiatives:
- ML projects demand trying every available tool to see what offers the best results, and there are numerous tools
- Tracking ML experiments is not easy as it involves tracking every configurable parameter, code, and data associated with the experiment
- It is hard to reproduce results without detailed tracking of experiments
- The plethora of deployment tools and environments make ML model productionalizing a challenging job
MLflow addresses the above challenges by bringing a better way to manage the entire ML lifecycle through its four components. It outperforms the internal platforms developed by enterprises by offering the following advantages:
- MLflow works with any language, ML library, and existing code
- Offers superior flexibility and scalability to serve single users as well as large organizations
- Scales to big data with Apache Spark
- Strong user community support
Key Features of MLflow
MLflow tracking
The MLflow experiment tracking component is an API and UI for logging parameters, metrics, code versions, and output files while executing ML code and later visualizing the outcomes. MLflow Tracking allows log and query experiments using Python, R API, REST, and Java API.
MLflow projects
It offers a format for reproducible runs using Docker and Conda, and helps share your ML code with others. This component provides API and command-line tools for running experiments and tracking them into workflows.
MLflow models
This component offers a model packaging format that allows deploying the same model to batch and real-time serving on - Apache Spark, Azure ML, or AWS SageMaker. It helps save the model in different flavors that are best understandable by downstream tools.
MLflow model registry
It is a centralized model store, UI, and set of APIs that help collectively manage the complete lifecycle of MLflow models.
