MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:
- MLflow Tracking: An API to log parameters, code, and
results in machine learning experiments and compare them using an interactive UI.
- MLflow Projects: A code packaging format for reproducible
runs using Conda and Docker, so you can share your ML code with others.
- MLflow Models: A model packaging format and tools that let
you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as
Docker, Apache Spark, Azure ML and AWS SageMaker.
- MLflow Model Registry: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.
I used it a bit during data science course and found it a really easy to use. In the next years, I expect to see rapid growth of ML development tools and therefore progressive category of the apps for Cloudron