The key components of a successful MLOps strategy revolve around having standards and best practices that will help you develop your ML service in a way that is almost production-ready from day one. It’s very important to have a production mindset even if you’re still in the development phase.
1. Tools & Frameworks
Choose the right tools for your data scientists, data engineers, and DevOps. These tools and frameworks should be used in both development and production environments, in a way that will smoothen out the transition between development and production.
2. Building Blocks
Build the right building blocks for:
- Data acquisition
- Feature engineering
- Model training
- Model serving
- Model monitoring
For example, feature stores can help with feature engineering, which, in many cases, is considered the hardest task when building ML pipelines. Feature stores are a catalog of features that data scientists and engineers can leverage. With feature stores, data scientists can reuse features instead of creating duplicate features. They also empower data scientists to run complex feature engineering tasks using a simple and abstract API.
A strategy that covers all these steps. So when you start you can feel confident you have the right best practices and standards to complete each step in a way that reduces the time it takes to get those components to production. The state of mind should be that the development process should be production-ready from the get-go.