Automated Model Training in CI/CD Pipelines
Automatically Retrain Models with GitHub Actions and DVC, and Share Evaluation Reports in Pull Requests Using CML. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 33)
100 Days of MLOps33
This roadmap takes you from zero MLOps knowledge to a production-ready ML engineer capable of building, deploying, monitoring, and automating end-to-end machine learning pipelines. You will master tools like MLflow, Docker, Kubernetes, Kubeflow, and cloud ML platforms while applying best practices in CI/CD, model governance, and observability. By Day 100, you will be able to design and operate scalable, reliable ML and LLM systems in real-world environments.
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GitHub Actions for ML CI Pipelines
Automate Testing, Training, Evaluation, and Deployment to Build Reliable Machine Learning Systems. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 32)
CI/CD Fundamentals for Machine Learning
Automate Testing, Training, Evaluation, and Deployment to Build Reliable Machine Learning Systems. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 31)
Running and Managing Kubeflow Pipeline Runs
Execute, Schedule, Monitor, and Troubleshoot Parameterized Machine Learning Pipelines Using the Kubeflow UI and Python SDK (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 30)
Building Kubeflow Pipelines with the Python SDK
Build, Connect, Compile, and Deploy Reusable Machine Learning Pipelines Using the Kubeflow Python SDK (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 29)
Week 4 Review: Kubernetes and Kubeflow Foundations
Review, Integrate, and Deploy a Complete Kubernetes-Based MLOps System with MLflow, FastAPI, HPA, and Kubeflow (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 28)