Introduction to MLOps answers the question of how to deploy a stable model?
In deploying machine learning models to the production environment, It is important to consider performance in the real world. Machine learning operation involves model testing, versioning, continuous deployment (CI/CD), availability, and monitoring.
How does a machine learning system work in production?
Explore these archives to grasp the what, why, and how to build machine learning systems.
These steps below are essential in building an excellent end-to-end machine learning system.
- In a typical MLE project, it is crucial first to define the scope of the project. The excellent project scope will define the outcome of the project.
- Data Engineering: this phase defines the methods and techniques used to collect, organize and store big data. Some other ways to clean and preprocess data.
- Build Machine learning models. At this phase, you already have the correct data defined in your scope documentation. Different machine learning algorithms are applied in training and testing a good model.
- Model deployment. Deploy models to connect with new or existing applications either natively or through application interfaces (API).
- Model monitoring and maintenance. It is crucial to monitor models in production. Monitor its performance over time and if there is a need to retrain based on new information.