ML Platforms
- (Photo: Princeton University, Office of Communications)
- ML Platforms
Here are some ML platforms:
- MLflow Model Registry: A central repository for storing and versioning ML models, allowing users to track lineage, compare models, and deploy them.
- Amazon SageMaker: A fully managed ML platform that runs on Amazon Web Services, allowing developers and data scientists to build, train, and deploy models in the cloud.
- Kubeflow: A Kubernetes-native ML platform that simplifies the model build, train, and deploy lifecycles across different infrastructures.
- Comet ML: A platform for tracking, comparing, explaining, and optimizing ML models and experiments, which can be used with any ML library.
- Neptune: A centralized metadata store for MLOps workflows that allows users to track, visualize, and compare thousands of models in one place
- MLflow
MLflow is an open-source platform that can manage the entire machine learning (ML) lifecycle, including experiments, model deployment, and a central model registry. It's library agnostic and can be deployed in any cloud.
MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps that other data scientists can use as a “black box,” without even having to know which library you are using.
MLflow can also help remove dependencies between models and tracking solutions, which can make training routines portable.
This allows ML practitioners to train and experiment with models locally, or use training routines from other platforms.
Azure Machine Learning (Azure ML) integrates with MLflow, allowing users to take advantage of MLflow's capabilities within the Azure ML ecosystem.
- The Four Primary Functions of MLflow
MLflow is an open source platform for managing the end-to-end machine learning (ML) lifecycle. It tackles four primary functions:
- Tracking experiments to record and compare parameters and results (MLflow Tracking).
- Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
- Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
- Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).
MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.
