ML and MLOps Fundamentals with Databricks and Transformers

Modern data science requires more than just training models; it demands robust engineering workflows, reproducible pipelines, and effective lifecycle management for large language models. This text-based course equips you with the foundational concepts and practical techniques needed to build end-to-end machine learning workflows using Databricks and Hugging Face Transformers. You will begin with essential terminology, core data infrastructure principles, and model lifecycle concepts before advancing to hands-on pipeline creation and model orchestration. What you'll learn: - Understand the fundamentals of machine learning operations (MLOps) and model lifecycle tracking - Configure and navigate Databricks workspaces for collaborative data science and model tracking - Work with Hugging Face Transformers to implement and fine-tune large language model workflows - Manage experiment tracking, model registry, and versioning with modern ML tooling - Apply basic retrieval-augmented generation (RAG) concepts and vector embeddings within data workflows - Deploy machine learning pipelines with automated testing and continuous monitoring best practices The course progresses systematically from fundamental data engineering concepts to practical MLOps workflows, ensuring you understand every phase of model development and deployment. Designed specifically for beginners, data analysts, and aspiring engineers, no prior MLOps experience is required to get started. Start reading today to build scalable, modern machine learning workflows.