Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.
This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.
This course will teach you to:
- Instrument a Jupyter notebook
- Manage hyperparameter config
- Log run metrics
- Collect artifacts for dataset and model versioning
- Log experiment results
- Trace prompts and responses to LLMs over time in complex interactions
When you complete this course, you will have a systematic workflow at your disposal to boost your productivity and accelerate your journey toward breakthrough results.
