From the course: MLOps Essentials: Model Development and Integration
Join today to access over 24,700 courses taught by industry experts.
Solution integration pipelines
From the course: MLOps Essentials: Model Development and Integration
Solution integration pipelines
- [Instructor] Once the model is ready and benchmarked, it needs to be integrated with the non ML part of the solution. In this chapter, we will focus on the processes and best practices for solution integration. ML models do not work standalone. They need to be embedded into other code to deliver end-to-end solutions. This requires integration with other code like APIs, UIs, databases, and microservices. How do we go about doing this integration? Let's look at the solution integration pipeline now. As seen before, we trained the model using input data from the feature store and hyper parameters. We then stored the model in the model registry. Now, the model and its pre-processing and post-processing code is in the form of a notebook. It needs to be converted into a software form that is suitable for integration. We call this step, notebook to software. This method produces the model in an executable form. This is then…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.
