You will build and deploy automated ETL pipelines using Apache Airflow to process multimodal data from raw sources into machine learning-ready features with proper error handling and monitoring.

You will explore the fundamentals of multimodal data validation, understanding why data quality is critical for AI system reliability and learning to identify common validation challenges across vision, audio, and language datasets.

You will implement practical validation solutions using Great Expectations and other industry tools, creating automated pipelines that detect and report multimodal data quality issues in production environments.

You will establish foundational understanding of test-driven development principles and modular architecture patterns specifically applied to machine learning code components.

You will implement production-quality DataLoader classes and training loops using TDD principles, creating comprehensive test suites and establishing CI/CD integration workflows.

You will systematically profile ML inference pipelines, identify performance bottlenecks, and apply optimization techniques like quantization and pruning to achieve real-time performance requirements.

You will compare Git branching strategies (GitFlow vs Trunk-Based Development), design CI/CD pipelines with automated testing and deployment, and implement version control workflows optimized for ML development teams.

You will learn the fundamentals of configuring cloud GPU clusters for distributed machine learning training, from understanding the strategic value to hands-on implementation of multi-node environments.

You will implement production-ready containerized deployment strategies with orchestration platforms, mastering the transition from development environments to scalable, maintainable ML systems.

You will learn the fundamentals of analyzing Kubernetes resource utilization patterns and identifying scaling opportunities through dashboard analysis and metric interpretation.

You will implement advanced Kubernetes scaling strategies, configure Horizontal Pod Autoscalers, and demonstrate mastery through comprehensive resource optimization scenarios.

You will build a production-grade multimodal ML system integrating automated data pipelines, optimized model training, and scalable cloud-native deployment.This capstone project synthesizes data engineering, ML development, and cloud infrastructure practices into a cohesive, real-world ML engineering system.

You will learn how GenAI copilots and automation tools accelerate multimodal ML engineering from scalable schema design and ETL pipeline generation to inference optimization and cloud cost management.