Feast Online Store and Real-Time Feature Serving
Serve Feast Features in Milliseconds, Keep Them Fresh, and Wire Them Into Your FastAPI Model at Inference Time.
100 Days of MLOps44
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.
Check out the latest posts
Feature Stores: Concepts and Use Cases
Understand How Feature Stores Organize, Reuse, and Serve ML Features for Training and Real-Time Predictions.
Week 5 Review: CI/CD for ML Projects
Automate ML Model Deployment to Kubernetes with GitOps, ArgoCD, and Helm.
Continuous Deployment: Automating ML Model Deployment
Automate ML Model Deployment to Kubernetes with GitOps, ArgoCD, and Helm.
Building and Pushing Docker Images in CI/CD
From Code to Container: Automate Docker Builds, Tagging, and Registry Pushes with GitHub Actions.
Automated Model Training in CI/CD Pipelines
Automatically Retrain Models with GitHub Actions and DVC, and Share Evaluation Reports in Pull Requests Using CML.