Detailed Course Outline
Introduction
Domain 1: Data Preparation for Machine Learning (ML)
- 1.1 Ingest and store data.
- 1.2 Transform data and perform feature engineering.
- 1.3 Ensure data integrity and prepare data for modeling.
Domain 2: ML Model Development
- 2.1 Choose a modeling approach.
- 2.2 Train and refine models.
- 2.3 Analyze model performance.
Domain 3: Deployment and Orchestration of ML Workflows
- 3.1 Select deployment infrastructure based on existing architecture and requirements.
- 3.2 Create and script infrastructure based on existing architecture and requirements.
- 3.3 Use automated orchestration tools to set up continuous integration and continuous delivery
- (CI/CD) pipelines.
Domain 4: ML Solution Monitoring, Maintenance, and Security
- 4.1 Monitor model interference.
- 4.2 Monitor and optimize infrastructure costs.
- 4.3 Secure AWS resources.
Course completion