Lilly Tech Systems
GitOps for ML Infrastructure
Master GitOps workflows for managing machine learning infrastructure at scale. Learn how to use ArgoCD and Flux to declaratively manage ML deployments, automate model rollouts, detect configuration drift, and implement production-grade CI/CD pipelines for ML systems using Git as the single source of truth.
What You'll Learn
This course covers GitOps principles and tools for managing ML infrastructure declaratively.
GitOps Principles
Understand declarative infrastructure, Git as source of truth, automated reconciliation, and continuous deployment for ML workloads.
ArgoCD & Flux
Deploy and configure ArgoCD and Flux for managing Kubernetes-based ML infrastructure with automated sync and rollback.
ML Manifests
Write Kubernetes manifests for ML workloads including training jobs, model serving, feature stores, and GPU scheduling.
Drift Detection
Detect and remediate configuration drift in ML environments, ensuring consistency between desired and actual state.
Course Lessons
Follow the lessons in order for a comprehensive understanding of GitOps for ML infrastructure.
1. Introduction
What is GitOps? Core principles, why GitOps matters for ML infrastructure, and comparison with traditional CI/CD approaches.
2. ArgoCD for ML
Install and configure ArgoCD for ML workloads. Application sets, sync policies, health checks, and multi-cluster management.
3. Flux for ML
Set up Flux CD with source controllers, Kustomize overlays, Helm releases, and image automation for ML pipelines.
4. ML Manifests
Design Kubernetes manifests for ML workloads: training jobs, model servers, feature pipelines, and GPU resource management.
5. Drift Detection
Implement drift detection for ML infrastructure: configuration drift, model drift triggers, automated remediation workflows.
6. Best Practices
Production GitOps patterns: repository structure, secrets management, multi-environment promotion, and disaster recovery.
Prerequisites
What you need before starting this course.
- Basic understanding of Kubernetes (pods, deployments, services)
- Familiarity with Git workflows (branches, pull requests, merges)
- Experience with YAML configuration files
- Basic knowledge of ML training and serving concepts