Further reading

For more information about the topics that were covered in this chapter, take a look at the following resources:

1. MLflow notebook experiment tracking in Databricks: https://docs.databricks.com/applications/mlflow/tracking.html#create-notebook-experiment
2. Building Multistep Workflows: https://www.mlflow.org/docs/latest/projects.html#building-multistep-workflows
3. End-to-end ML pipelines with MLflow projects: https://dzlab.github.io/ml/2020/08/09/mlflow-pipelines/
4. Installing a privately built Python package: https://medium.com/@ffreitasalves/pip-installing-a-package-from-a-private-repository-b57b19436f3e
5. Versioning data and models in ML projects using DVC and AWS: https://medium.com/analytics-vidhya/versioning-data-and-models-in-ml-projects-using-dvc-and-aws-s3-286e664a7209
6. Introducing Delta Time Travel for Large Scale Data Lakes: https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html
7. How We Won...