Deploying GenAI Models in Production (MLOps for GenAI)

Apply DevOps-style pipelines to build, test, and deploy GenAI models.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

Overview

This course focuses on the application of MLOps principles specifically tailored for Generative AI models. Participants will learn how to build, test, deploy, and monitor GenAI models in production environments using automated, scalable pipelines. The course covers best practices for continuous integration, delivery, and governance to ensure reliable and secure AI services.

Learning Objectives

Participants will be able to:

• Understand the unique challenges of deploying GenAI models

• Design and implement MLOps pipelines for GenAI workflows

• Automate model training, validation, and deployment processes

• Monitor model performance and manage version control

• Ensure compliance, security, and scalability in production

Target Audience

• AI engineers and data scientists

• DevOps and MLOps specialists

• Cloud architects and platform engineers

• Technical leads and AI project managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Hands-on labs with MLOps tools and frameworks

• Step-by-step pipeline development exercises

• Case study reviews and group discussions

Materials Provided

• Pipeline templates and configuration files

• Monitoring and alerting guides

• Best practices documentation for MLOps in GenAI

• Certificate of completion

Outcomes

• Build end-to-end automated pipelines for GenAI deployment

• Maintain model quality and reliability in production environments

• Implement continuous monitoring and version control

• Manage risk through secure and compliant deployment processes

Outline / Content

Day 1: Introduction to MLOps for GenAI

• Overview of MLOps concepts and challenges with GenAI

• Architecture of GenAI deployment pipelines

• Tools and platforms supporting GenAI MLOps

Day 2: Building and Testing Pipelines

• Automating data preprocessing and model training

• Validation, testing, and quality assurance workflows

• Continuous integration for GenAI models

Day 3: Deployment and Monitoring

• Deploying models to cloud and edge environments

• Monitoring performance, drift detection, and logging

• Incident management and rollback strategies

Day 4: Governance and Scaling

• Security, compliance, and audit in MLOps

• Scaling pipelines for large-scale GenAI applications

• Workshop: Build and deploy a GenAI model pipeline

• Group presentations and feedback