Exploring experiments
As the name suggests, experiments are the central location where all the model training pertinent to business problems can be accessed. Users can define their name for the experiment or a default system-generated one and use it to train the different ML model training runs. Experiments in the Databricks UI come from integrating MLflow into the platform. We will dive deeper into MLflow in the coming chapters to understand more details; however, it’s important to get a sense of what MLflow is and some of the terminology that is MLflow-specific.
MLflow is an open source platform for managing the end-to-end ML life cycle. Here are the key components of MLflow:
- Tracking: This allows you to track experiments to record and compare parameters and results.
- Models: This component helps manage and deploy models from various ML libraries to a variety of model serving and inference platforms.
- Projects: This allows you to package ML code in a reusable...
