The research and development (R&D) phase of building an AI model to address a business problem is characterized by rapid exploration and iteration. Everything is on the table and experimentation is encouraged, from understanding how to frame the problem, to determining how to most effectively use the data on hand, to discovering the model architecture with the best performance. In stark contrast to this, the operationalization phase of AI model development requires that the model be completely characterized, produce reproducible results, and be stable for integration in automation processes. Model versioning best practices and version control tools are essential to successfully navigating and overcoming this gap between R&D and production engineering. The practice of version control is nothing new.

In this installment of the ModelOps Blog Series, we will transition from what it takes to build AI models to the process of deploying into production. Think of this as the on ramp for extracting value from your AI investments--moving your model out of the lab and into an environment where it can provide new insights for your organization or add value to customers. Front and center is the concept of continuous integration (CI) and continuous deployment (CD). This methodology can be applied to automate the process of releasing AI models in a reproducible and reliable manner. Get ready to walk away with everything you need to know in order to leverage containers to formalize and manage AI models within your organization.