- Lab
- Core Tech
GitOps for AI/ML Workflow Automation
In this hands-on lab, you'll implement GitOps principles to automate AI/ML workflows using CLI tools and GitHub Actions. You'll set up a lightweight ML pipeline with MinIO for storage, deploy a simple ML model using Docker, and implement automated monitoring with rollback capabilities—all through command-line interfaces.
Path Info
Table of Contents
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Challenge
Automate AI/ML Workflow Components using GitOps Principles
- Integrate MinIO for object storage and Weaviate for AI-enhanced metadata management.
- Orchestrate AI/ML services using Docker Compose and GitHub Actions.
- Programmatically interact with AI/ML components (for example a schema definition, data indexing, and backups).
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Challenge
Implement CI/CD for ML Model Development and Deployment
- Automate ML pipeline stages (data preparation, model training, evaluation) with GitHub Actions.
- Version and track ML models and data using appropriate tools.
- Deploy containerized ML applications on Kubernetes.
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Challenge
Leverage Self-Healing Principles in AI/ML GitOps Pipelines
- Implement continuous monitoring and drift detection for ML infrastructure.
- Configure automated rollbacks for faulty ML model deployments.
- Explore AI-driven anomaly detection and automated remediation in CI/CD pipelines.
What's a lab?
Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.
Provided environment for hands-on practice
We will provide the credentials and environment necessary for you to practice right within your browser.
Guided walkthrough
Follow along with the author’s guided walkthrough and build something new in your provided environment!
Did you know?
On average, you retain 75% more of your learning if you get time for practice.