The Machine Learning DevOps Engineer Nanodegree program focuses on the software engineering fundamentals needed to successfully streamline the deployment of data and machine-learning models in a production-level environment. Students will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring over time.
Students who graduate from the program will be able to:
- Implement production-ready Python code/processes for deploying ML models outside of cloud-based environments facilitated by tools such as AWS SageMaker, Azure ML, etc.
- Engineer automated data workflows that perform continuous training (CT) and model validation within a CI/CD pipeline based on updated data versioning
- Create multi-step pipelines that automatically retrain and deploy models after data updates
- Track model summary statistics and monitor model online performance over time to prevent model-degradation
