MLOps & LLMOps

MLOps & LLMOps

Duration: 3 Days

Description

Getting a model working is only the beginning; running it reliably in production is the discipline of MLOps and, for language models, LLMOps. This course teaches the practices and tooling needed to deploy, monitor, and maintain ML and LLM systems: versioning data and models, building reproducible pipelines, deploying and serving, monitoring for drift and quality, and managing the unique operational challenges of generative AI. Hands-on exercises build practical skills for taking models from notebook to dependable production service.
Class time is divided between instructor-led explanation (40 percent) and hands-on exercises (60 percent).

Audience

ML and data engineers, developers, and platform engineers responsible for deploying and operating ML or LLM systems in production. Assumes prior ML or AI development experience.

Objectives

  • Explain the goals and scope of MLOps and LLMOps
  • Version and manage data, models, and pipelines
  • Build reproducible training and deployment workflows
  • Deploy and serve models reliably
  • Monitor for performance, drift, and quality
  • Address the unique operational needs of LLM systems

Prerequisites

Participants should have prior ML or AI development experience. The following will help:

  • Working knowledge of Python (see Python Foundations)
  • Machine learning foundations (see Machine Learning Foundations) or AI development experience
  • Basic familiarity with cloud or deployment concepts

Course Outline

  • Module 1 – Course Introduction
    • Welcome and objectives
    • Why MLOps and LLMOps
    • The production ML lifecycle
  • Module 2 – Versioning and Reproducibility
    • Versioning data and models
    • Experiment tracking
    • Reproducible environments and pipelines
    • Hands-on exercise: track an experiment
  • Module 3 – Deployment and Serving
    • Packaging models for deployment
    • Serving patterns: batch, online, and streaming
    • APIs and scaling
    • Hands-on exercise: deploy a model service
  • Module 4 – Monitoring and Maintenance
    • Monitoring performance and health
    • Data and model drift
    • Retraining and rollback strategies
    • Alerting and incident response
  • Module 5 – LLMOps Specifics
    • Operating LLM systems versus traditional ML
    • Prompt and version management
    • Evaluating LLM quality in production
    • Cost, latency, and token management
  • Module 6 – Governance and Reliability
    • Pipelines, automation, and CI/CD for ML
    • Security and access control
    • Auditability and compliance
    • Hands-on exercise: an automated pipeline
  • Module 7 – Course Wrap-Up
    • Key takeaways
    • Resources for continued learning
    • Next steps