Chapter 2 Ops Tools & Principles

MLOps integrates a range of DevOps techniques and tools to enhance the development and deployment of machine learning models. By promoting cooperation between development and operations teams, MLOps strives to improve communication, enhance efficiency, and reduce delays in the development process. Advanced version control systems can be employed to achieve these objectives.

Automation plays a significant role in achieving these goals. For instance, CI/CD pipelines streamline repetitive tasks like building, testing, and deploying software. The management of infrastructure can also be automated, by using infrastructure as code to facilitate an automated provisioning, scaling, and management of infrastructure.

To enhance flexibility and scalability in the operational process, containers and microservices are used to package and deploy software. Finally, monitoring and logging tools are used to track the performance of deployed and containerized software and address any issues that arise.