This Specialization equips you with the skills to design, build, and deploy production-ready machine learning systems from end to end. You'll learn to architect custom neural networks, optimize deep learning models, engineer robust data pipelines, and implement MLOps best practices including CI/CD, automated testing, documentation, and cloud deployment. Through hands-on projects using PyTorch, TensorFlow, FastAPI, and cloud platforms like AWS SageMaker, you'll gain practical experience building scalable AI systems that meet real-world performance, reliability, and governance requirements.
Applied Learning Project
Throughout this Specialization, you will complete hands-on projects that mirror real-world ML engineering workflows. You'll design and build custom neural networks in PyTorch, optimize models for deployment using TensorFlow Lite and quantization techniques, and construct ETL pipelines with tools like Airflow and Spark. Projects include building testable Python packages with pytest, developing FastAPI microservices that serve transformer models, creating comprehensive API documentation with MkDocs, and configuring automated regression test suites. You'll also deploy and optimize ML workloads on cloud platforms like AWS SageMaker, design scalable system architectures with monitoring stacks, and integrate AI services using gRPC and Prometheus. These projects prepare you to build, test, document, and deploy production-grade AI systems.
