A checklist for MLOps process implementation
Artificial intelligence (AI) and machine learning (ML) are essential for today’s organizations, and data is just as critical to applications as the code they are built on. However, there is still a lack of collaboration between the different groups involved in the development of AI/ML-driven applications. To effectively use AI/ML and data science in deployable applications, companies must bring together developers, IT operations, data engineers, data scientists, and ML engineers to operationalize machine learning operations (MLOps).
In this checklist, we’ll explore how to implement MLOps processes, including:
- Building a data strategy.
- Providing self-service access to tools.
- Creating a collaborative environment.
