Getting Started with MLflow
For those new to MLflow or seeking a refresher on its core functionalities, the quickstart tutorials here are the perfect starting point. They will guide you step-by-step through fundamental concepts, focusing purely on a task that will maximize your understanding of how to use MLflow to solve a particular task.
Logging your first MLflow Model
In this quickstart tutorial, you will walk through the basics of MLflow in a sequential and guided manner. With each subsequent step, you will increase your familiarity with the primary functionality around MLflow Tracking and how to navigate the MLflow UI.
If you would like to get started immediately by interactively running the notebook, you can:
Download the NotebookGuide sections
Interested in navigating directly to the content that you’re curious about? Select the section from each tutorial below!
Learn how to start a MLflow Tracking Server and the MLflow UI Server locally
Connect to the Tracking Server with the MLflow Client and learn to search for experiments
Explore the MLflow UI and create your first MLflow experiment with a unique name and identifying tags
Learn the benefits of relevant identifying tags as you execute a search for experiments containing identifying tag values
Build a synthetic dataset to use while exploring the features of MLflow
Train a model using the synthetic dataset and log the trained model, metrics, and parameters
See the tutorial notebook in its entirety. If you prefer just reading code, this is the best place to look.
15 minute Quickstart - Learn the basics of MLflow
In this rapid-pace quickstart, you will be exposed to the autologging feature in MLflow to simplify the logging of models, metrics, and parameters. After training and viewing the logged run data, we’ll load the logged model to perform inference, showing core features of MLflow Tracking in the most time-efficient manner possible.
Train a model and use MLflow autologging to automatically record model artifacts, metrics, and parameters
See what autologging will autonomously log for you during model training with only a single line of code
Load the autologged model in its native format and use it to generate predictions
15 minute Quickstart - Comparing Runs and Deploying your Best Model
This quickstart tutorial focuses on the MLflow UI’s run comparison feature, provides a brief overview of MLflow Projects, and shows how to register a model. After locally serving the registered model, a brief example of preparing a model for remote deployment via containerizing the model via Docker is covered.
Run an MLflow Project that will perform hyperparameter tuning to generate a large volume of runs
Use the MLflow UI Runs Compare functionality to evaluate the hyperparameter tuning run and select the best model
Learn to register models with the MLflow UI and perform stage transitions from within the UI
Use the integrated inference server in MLflow to serve your registered model locally
Learn how to generate a docker container that houses your model for deployment to external services