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Master privacy-conscious AI development by integrating secure coding practices, vulnerability scanning, and AI assistants like Claude Code and Gemini CLI to protect production applications.
Master prompt architecture and advanced NLP on Amazon Bedrock, covering token processing, versioned prompt templates, chain-of-thought agents, and custom Rust NLP tools for production deployments.
Master production-ready data pipelines using Delta Live Tables and Medallion Architecture on Databricks through hands-on labs and a complete inventory management capstone project.
Discover how to build data pipelines on Databricks Lakehouse Platform with hands-on PySpark, Delta Lake, and Unity Catalog training for complete data engineering workflows.
Master production GenAI systems on Databricks through prompt engineering, RAG pipelines, model governance, and code intelligence with hands-on projects and evaluation metrics.
Master MLflow and Databricks to build, track, and deploy production-ready machine learning models while solving the reproducibility crisis through structured experiment tracking.
Master Databricks governance, Unity Catalog, CI/CD workflows, and MLOps practices to build secure, reliable data pipelines and AI applications with hands-on tools and real-world code.
Master GitHub Enterprise administration, covering SAML SSO, access permissions, security compliance, GitHub Actions at scale, and package management with hands-on demos.
Build a complete production app using GitHub Copilot, covering API design, business logic, database persistence, testing, and code review in a real-world development workflow.
Master advanced GitHub Copilot techniques—multi-turn interactions, iterative refinement, and context control with @workspace and #file markers—to produce production-quality code efficiently.
Master cloud-based dev environments with GitHub Codespaces, GPU-accelerated AI workloads, Copilot-assisted coding, and automated CI/CD pipelines using GitHub Actions.
Explore GitHub Models to evaluate, select, and integrate AI models, build FastAPI endpoints, manage rate limits, and deploy production-ready AI applications using GitHub Codespaces and Azure AI.
Master AI code governance with GitHub Copilot—validate, audit, and secure AI-generated code, configure custom rules, and select the right LLM for production workflows.
Master GitHub security by configuring 2FA, managing permissions with least-privilege principles, and implementing enterprise governance controls for accounts, repositories, and organizations.
Master transformer fine-tuning with Hugging Face ecosystem, from data preprocessing to model deployment, building production-ready sentiment analysis systems.
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