- Learn how to find the best classification model with automated machine learning (AutoML). You'll use the Python SDK (v2) to configure and run an AutoML job.
In this module, you'll learn how to:
- Prepare your data to use AutoML for classification.
- Configure and run an AutoML experiment.
- Evaluate and compare models.
- Learn how to use MLflow for model tracking when experimenting in notebooks.
In this module, you'll learn how to:
- Configure to use MLflow in notebooks
- Use MLflow for model tracking in notebooks
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Syllabus
- Find the best classification model with Automated Machine Learning
- Introduction
- Preprocess data and configure featurization
- Run an Automated Machine Learning experiment
- Evaluate and compare models
- Exercise - Find the best classification model with Automated Machine Learning
- Module assessment
- Summary
- Track model training in Jupyter notebooks with MLflow
- Introduction
- Configure MLflow for model tracking in notebooks
- Train and track models in notebooks
- Exercise - Track model training
- Module assessment
- Summary