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AI-Powered Automation Test Engineer Program

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Course Content

Module 1: MLOps Foundations
Grasp MLOps vs. DevOps and master testing pipelines for reliable ML. Navigate the full ML lifecycle from experimentation to retraining. Apply agile methodologies for iterative, production-ready ML projects.

Module 2: Version Control & Collaboration
Master Git essentials for ML – commits, branches, and merges made simple. Structure repos for teams with data versioning and experiment tracking. Handle pull requests & reviews to keep ML projects collaborative and clean.

Module 3: Model Packaging & Experiment Management
Track experiments with MLflow – log metrics, params, and artifacts effortlessly. Package models for deployment using serialization and virtual environments. Build automated pipelines for training, validation, and testing workflows.

Module 4: Building ML Applications
Create ML APIs with Flask/FastAPI for real-time predictions. Develop Streamlit apps for interactive dashboards and quick prototypes. Design inference pipelines that scale from batch to live serving.

Module 5: CICD for ML Models
Automate CICD pipelines tailored for ML – from training to deployment. Integrate testing gates for safe releases, rollbacks, and canary deploys. Streamline cloud workflows with versioned, reproducible ML ops.

Module 6: Cloud-Based MLOps Deployment
Deploy on AWS, Azure, GCP with Docker containers and Kubernetes scaling. Monitor production models – track drift, logs, and auto-retrain triggers. Secure ML systems with governance, compliance, and high-availability setups

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