MLFlow 2.8.1

MLFlow

Description:

• Managing machine learning life cycle

Core Components of MLflow

• MLflow tracking
• MLflow model registry
• MLflow models
• MLflow evaluate:
  • Designed for in-depth model analysis, this set of tools facilitates objective model comparison, be it traditional ML algorithms or cutting-edge LLMs.
• Prompt Engineering UI:
  • A dedicated environment for prompt engineering, this UI-centric component provides a space for prompt experimentation, refinement, evaluation, testing, and deployment.
  • In experimental
• Recipes:
  • Serving as a guide for structuring ML projects, Recipes, while offering recommendations, are focused on ensuring functional end results optimized for real-world deployment scenarios.
• Projects:
  • MLflow Projects standardize the packaging of ML code, workflows, and artifacts, akin to an executable.
  • Each project, be it a directory with code or a Git repository, employs a descriptor or convention to define its dependencies and execution method.
• AI Gateway: experimental
  • This server, equipped with a set of standardized APIs, streamlines access to both SaaS and OSS LLM models. It serves as a unified interface, bolstering security through authenticated access, and offers a common set of APIs for prominent LLMs.

mlflow Python

mlflow CLI