Generative AI Application Deployment and Monitoring
Contact us to book this course
Learning Track
Generative AI
Delivery methods
On-Site, Virtual
Duration
1/2 day
Ready to learn how to deploy, operationalize, and monitor generative AI applications? This content will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.
Objectives
- Explain best practices for deploying generative AI applications using tools like Model Serving.
- Explain how to operationalize generative AI applications following best practices and recommended architectures.
- Use Lakehouse Monitoring to monitor generative AI applications and their components.
Prerequisites
- Familiarity with natural language processing concepts
- Familiarity with prompt engineering/prompt engineering best practices
- Familiarity with the Databricks Data Intelligence Platform
- Familiarity with RAG (preparing data, building a RAG architecture, concepts like embedding, vectors, vector databases, etc.)
- Experience with building LLM applications using multi-stage reasoning LLM chains and agents
- Familarity with Databricks Data Intelligence Platform tools for evaluation and governance.
Course outline
- Model Management
- Deployment Methods
- Introduction to Batch Deployment
- Batch Inference
- Batch Inference Workflows using SLM
- Introduction to Real-Time Deployment
- Databricks Model Serving
- Serving External Models with Model Serving
- Deploying an LLM Chain to Databricks Model Serving
- Custom Model Deployment and A/B Testing
- AI Application Monitoring
- Online Monitoring an LLM RAG Chain
- MLOps Primer
- LLMOps vs MLOps
