Generative AI Engineer

DATB-GAIDM : Generative AI Application Deployment and Monitoring

Course Duration

2 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

The course covers details about how to deploy, operationalize, and monitor generative AI applications. The course will help you gain skills in the deployment of generative AI applications using tools like Model Serving. It also covers how to operationalize generative AI applications following best practices and recommended architectures. Finally, the course discusses the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

What You'll Learn

Model Deployment Fundamentals
Batch Deployment
Real-Time Deployment
AI System Monitoring
LLMOps Concepts

Who Should Attend

Data scientists, ML engineers and AI practitioners responsible for deploying, serving and monitoring generative-AI applications in production.
Professionals managing real-time inference workloads, model-serving endpoints, versioning, rollout strategies and operational readiness for LLM-based systems.
Individuals implementing monitoring pipelines for LLM performance, latency, drift, hallucination detection, cost optimisation and quality evaluation.
Practitioners with experience in Python, MLflow or model-serving frameworks who want to enhance their skills in operationalising and governing generative-AI systems.
Teams moving generative-AI projects from experimentation to production, ensuring reliability, observability, compliance and continuous improvement of AI applications.

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.

Learning Journey

Coming Soon...

Module 1. Model Deployment Fundamentals

Model Management
Deployment Methods

Module 2. Batch Deployment

Introduction to Batch Deployment
Batch Inference
Batch Inference Workflows using SLM

Module 3. Real-Time Deployment

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

Module 4. AI System Monitoring

AI Application Monitoring
Online Monitoring an LLM RAG Chain

Module 5. LLMOps Concepts

MLOps Primer
LLMOps vs MLOps

Frequently Asked Questions (FAQs)

None

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