Summary

In this chapter, we reviewed the core tools used across the book. First, we understood how to install the correct version of Python that supports our repository. Then, we looked over how to create a virtual environment and install all the dependencies using Poetry. Finally, we understood how to use a task execution tool like Poe the Poet to aggregate all the commands required to run the application.

The next step was to review all the tools used to ensure MLOps best practices, such as a model registry to share our models, an experiment tracker to manage our training experiments, an orchestrator to manage all our ML pipelines and artifacts, and metadata to manage all our files and datasets. We also understood what type of databases we need to implement the LLM Twin use case. Finally, we explored the process of setting up an AWS account, generating an access key, and configuring the AWS CLI for programmatic access to the AWS cloud. We also gained a deep understanding of...