This content originally appeared on DEV Community and was authored by Srinivasaraju Tangella
Introduction
DevOps has transformed the way software is built, tested, and deployed. With the rise of AI and Machine Learning, the next evolution is clear: combining DevOps with Generative AI and MLOps.
Imagine your CI/CD pipeline not just building and deploying applications, but also intelligently predicting failures, optimizing deployments, and automating repetitive tasks. This is where AI-powered DevOps comes in.
In this guide, we’ll walk through a complete learning path, covering everything from the basics to cloud-native AI/ML pipelines, and give practical use cases that DevOps engineers can implement today.
Module 1: Foundations
DevOps Basics: Continuous Integration, Continuous Deployment, and monitoring workflows.
Generative AI: Overview of GPT, DALL·E, and other models.
MLOps: Understanding the lifecycle of ML models—from data to deployment.
Industry Relevance: How AI is being applied in DevOps pipelines.
Narration:
Think of this as setting the stage. Before you build pipelines or deploy models, you need to understand why AI in DevOps matters. The key benefit is automation at intelligence scale—your pipelines don’t just run, they adapt.
Module 2: Linux and Cloud for AI/DevOps
Linux essentials for managing servers and AI workloads.
Cloud setup (AWS, GCP, Azure) for AI pipelines.
Networking and security basics for AI deployment.
Narration:
Your pipelines will mostly run on Linux servers and cloud infrastructure. Setting up the environment correctly is crucial for stability and scalability.
Module 3: Core DevOps for AI
Version Control: Git workflows for AI projects.
Docker: Containerizing AI/ML applications.
Kubernetes: Deploying scalable AI workloads.
CI/CD: Integrating AI models into pipelines.
Narration:
Containerization ensures your AI models behave the same way in development, testing, and production. Kubernetes helps scale your applications automatically when traffic spikes or workloads increase.
Module 4: Generative AI Integration
Integrating GPT models into pipelines.
Automating code reviews and tests using AI.
AI-assisted monitoring and anomaly detection.
Narration:
Generative AI doesn’t just generate content—it can analyze code, predict issues, and improve deployment workflows. Imagine having a virtual AI assistant for your DevOps team.
Module 5: MLOps Essentials
ML lifecycle: Data → Training → Deployment → Monitoring.
Tools: MLflow, Kubeflow, TFX.
Dataset and model versioning.
Continuous retraining and deployment pipelines.
Narration:
MLOps is DevOps for machine learning. Models evolve just like software, so pipelines need to handle continuous updates, testing, and deployment safely.
Module 6: AI/ML CI/CD Pipelines
Automating ML model testing.
Containerizing models with Docker.
Blue-green and canary deployments for AI models.
Narration:
Your ML pipeline is like a production line for intelligence. CI/CD ensures models are always up-to-date and reliable before reaching users.
Module 7: Monitoring and Observability
Prometheus + Grafana for AI/ML pipelines.
Logging, tracing, and performance metrics.
Predictive monitoring using AI.
Narration:
Monitoring AI pipelines isn’t optional—it’s critical. Intelligent pipelines can self-heal, predict failures, and optimize performance automatically.
Module 8: Security and Governance
Secrets management and model access control.
Compliance and auditing.
DevSecOps for AI/ML workloads.
Narration:
AI pipelines often handle sensitive data. Security, governance, and compliance are not optional—they ensure trust and reliability in production.
Module 9: Cloud-Native AI/ML Tools
AWS SageMaker, GCP Vertex AI, Azure ML.
Serverless deployments: Lambda, Functions.
Cost optimization strategies for AI workloads.
Narration:
Cloud-native tools simplify AI deployment. You focus on intelligence and pipelines, while the cloud handles scalability, high availability, and resource management.
Module 10: Capstone Projects
Project 1: CI/CD pipeline for a Generative AI app.
Project 2: Deploy ML models in Kubernetes with monitoring.
Project 3: Integrate AI for code review and testing.
Project 4: End-to-end MLOps workflow with automated retraining.
Narration:
These projects let you apply everything learned. By the end, you can confidently build intelligent, cloud-native AI pipelines ready for real-world production environments.
Conclusion
By combining DevOps, Generative AI, and MLOps, engineers can create intelligent, self-optimizing pipelines. This course bridges traditional DevOps practices with the AI revolution, giving you a competitive edge in modern software engineering.
This content originally appeared on DEV Community and was authored by Srinivasaraju Tangella