GitHub Actions for ML CI Pipelines
Automate Testing, Training, Evaluation, and Deployment to Build Reliable Machine Learning Systems.
Sandip Das
Senior Cloud, DevOps, MLOps & ML Platform Engineer | Heading Cloud, DevOps & MLOps for start-ups | AWS Container Hero | Educator | Mentor | Teaching Cloud, DevOps & Programming in Simple Way
Check out the latest posts
Run real AWS commands with no AWS account, no credentials, no bill
Local Cloud Labs are live — 18 hands-on AWS labs against a real emulator on your own machine. 10 of them are free for everyone.
CI/CD Fundamentals for Machine Learning
Automate Testing, Training, Evaluation, and Deployment to Build Reliable Machine Learning Systems.
Running and Managing Kubeflow Pipeline Runs
Execute, Schedule, Monitor, and Troubleshoot Parameterized Machine Learning Pipelines Using the Kubeflow UI and Python SDK
Building Kubeflow Pipelines with the Python SDK
Build, Connect, Compile, and Deploy Reusable Machine Learning Pipelines Using the Kubeflow Python SDK
Week 4 Review: Kubernetes and Kubeflow Foundations
Review, Integrate, and Deploy a Complete Kubernetes-Based MLOps System with MLflow, FastAPI, HPA, and Kubeflow