Tokens to Deployment: NLP, Language Models, & Production API

About this course

This comprehensive program takes you through the complete lifecycle of building and deploying natural language processing and multimodal AI systems. From tokenization fundamentals to production API design, you'll develop the technical depth to build AI-powered systems that are reliable, scalable, and enterprise-ready. Starting with transformer architecture and NLP preprocessing, you'll progress through hands-on courses covering multimodal data pipelines, model evaluation, inference optimization, and production-grade API design. Each course emphasizes real-world workflows using industry-standard tools including Hugging Face, spaCy, PyTorch, TensorFlow, Apache Airflow, and Great Expectations, ensuring your skills translate directly to professional ML engineering roles. You'll learn to fine-tune BERT models for domain-specific tasks, build automated ETL pipelines for multimodal data, validate data quality at scale, and implement OAuth2-secured APIs with comprehensive OpenAPI documentation. The program also covers critical software engineering practices including test-driven development, CI/CD pipelines, and GitFlow strategies that make ML codebases maintainable and production-ready. By program completion, you'll possess the end-to-end skills to take an NLP or multimodal AI system from raw data to a deployed, optimized, and documented production service, making you a versatile and highly capable ML engineer.