Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

Production machine learning systems don't run on model accuracy alone — they depend on reliable data pipelines, optimized inference, and scalable cloud infrastructure. This course integrates the full stack of ML engineering skills needed to build and operate multimodal AI systems in the real world. You will design a unified feature store schema for image, audio, and text data, then automate ingestion and validation using Apache Airflow and Great Expectations. You will apply test-driven development to PyTorch data loaders and training loops, optimize a model for real-time inference using TensorRT, and manage your codebase with GitFlow and CI/CD pipelines. Finally, you will containerize and deploy a GPU-accelerated service to Kubernetes, tuning autoscaling to meet production performance targets. By the end, you will have a portfolio-ready project demonstrating end-to-end ML infrastructure skills — exactly what employers look for in ML Infrastructure Engineers, MLOps Engineers, and senior ML practitioners.

Syllabus

Create Unified Data Schema for Multimodal ML Features

You will design and implement unified data schemas that efficiently store and organize multimodal machine learning features across text, image, and audio data types.

Implement Automated ETL Pipelines with Workflow Orchestration

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.

Understanding Multimodal Data Validation

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.

Implementing Validation Frameworks

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.

Foundation - TDD Principles & ML Code Architecture

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

Implementation - DataLoader & Training Loop Development

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

Analyze inference code to optimize for real-time performance

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

Evaluate Git branching strategies and CI/CD pipelines for codebase management

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.

GPU Cluster Configuration for Distributed Training

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.

Containerization and Orchestration Implementation

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

Resource Utilization Analysis and Scaling Foundations

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

Advanced Scaling Optimization and Assessment

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

Project: Production-Ready Multimodal ML Engineering

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.

GenAI: GenAI-Enhanced Multimodal ML Engineering

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.