Production-Ready Multimodal ML Engineering
By External Provider
About this course
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.
What you will learn
- Design a multimodal feature store and build automated ETL pipelines using BigQuery and Airflow.
- Write test-driven ML training code and validate multimodal datasets for production readiness.
- Optimize model inference with TensorRT and manage ML codebases using GitFlow and CI/CD tools.
- Deploy GPU-accelerated services on Kubernetes and tune autoscaling for real-time performance.