Evaluate the model, including responsible AI guidelines
Use automated machine learning for tabular data
Use automated machine learning for computer vision
Use automated machine learning for natural language processing (NLP)
Select and understand training options, including preprocessing and algorithms
Evaluate an automated machine learning run, including responsible AI guidelines
Track model training by using MLflow
Develop code by using a compute instance
Train a model by using Python SDKv2
Use the terminal to configure a compute instance
Define the primary metric
Define early termination options
Configure job run settings for a script