AI and LLM Solutions Building on Databricks: key concepts of the end-to-end AI lifecycle, from data preparation and model building to deployment, monitoring, and MLOps

• Data preparation for model training: Learn how to load and process data for AI workloads.
• Feature engineering: Use Unity Catalog to create feature tables and track feature lineage.
• Training and registration of models: Use AutoML for efficient training and tuning of ML models.
• Production real-time or batch serving: Start using model serving for real-time workloads or deploy MLflow models for offline inference.
• Self-hosting large language models (LLMs): Securely and cost-effectively host open-source LLMs within the Databricks environment.
• Monitoring deployed models: Continuously capture and log Model Serving endpoint inputs and predictions.
• Bundle assets for programmatic deployment: Use Databricks Asset Bundles for efficient packaging and deployment of data and AI assets.
• End-to-end MLOps: Combine DataOps, ModelOps, and DevOps for end-to-end ML and LLM operations.
• Build LLM-powered RAG solutions: Leverage enterprise data to create accurate Q&A chatbots.