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SAAS Tools for MlFlow
These platforms streamline the entire machine learning lifecycle, often integrating MLflow’s capabilities.
Amazon SageMaker: AWS’s comprehensive, fully-managed platform that covers the entire ML workflow from data preparation to deployment and monitoring.
Google Vertex AI: Google Cloud’s unified platform for building, deploying, and scaling ML models, which includes MLOps tools like pipelines, a model registry, and monitoring.
Microsoft Azure Machine Learning: A cloud service that provides a range of tools and a unified environment to accelerate and manage the ML project lifecycle, with strong native MLflow integration.
Databricks (Managed MLflow): Databricks, co-founded by the creators of MLflow, offers a fully managed and enhanced version of MLflow tightly integrated with their lakehouse platform.
Benefits
Enhanced Collaboration: Provides a shared, centralized platform (via the Tracking Server and Model Registry UI) where data scientists can log, view, compare, and share experiment results and model versions.
Efficient Model Lifecycle Management: The Model Registry offers governance and an audit trail by controlling the transition of model versions through different stages (e.g., from Staging to Production) and linking them to their original training runs.