You'll get hands-on training in:
✅ PySpark & Data pipelines: Process batch and streaming data at scale, build and orchestrate reliable ETL/ELT pipelines with Databricks.
✅ DevOps fundamentals: Version control with Git, containerization with Docker, and CI/CD pipelines (GitHub Actions, Azure DevOps, or GitLab CI) - the backbone of production-grade engineering.
✅ APIs & system integration: Consume and expose REST APIs, handle authentication and error handling, and connect data systems end-to-end.
✅ AI-ready data preparation: Parse, chunk, clean, and enrich documents (PDFs, Word, HTML, scanned files) with the metadata that makes them retrievable, governed, and trustworthy.
✅ Embeddings & vector databases: Generate embeddings, load them into vector stores like Azure AI Search or PGVector, and understand why embedding quality determines retrieval quality.
✅ LLMs, prompting & RAG: Call LLMs, design prompts, produce structured outputs, and build Retrieval-Augmented Generation pipelines end-to-end - ingest → chunk → embed → index → retrieve → prompt → respond → evaluate.