Introduction to Kubeflow for ML Pipelines
Build, Orchestrate, Visualize, and Automate Reproducible Machine Learning Workflows on Kubernetes (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 27)
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.
Check out the latest posts
Introduction to Helm for ML Application Packaging
Package, Configure, Deploy, Upgrade, and Roll Back ML Applications on Kubernetes with Helm (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 26)
ML Model inference Scaling on Kubernetes Using Horizontal Pod Autoscaling and Rolling Updates
Make ML Models running on Kubernetes survive a traffic spike, and ship a new model version without a single failed prediction (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 25)
Kubernetes ConfigMaps, Secrets, and Persistent Volumes for ML Models
Managing Application Configuration, Credentials, and Persistent Model Storage in Kubernetes (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 24)
Deploying ML Models on Kubernetes
From Docker Containers to Production-Ready Kubernetes Deployments (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 23)
Introduction to Kubernetes for MLOps Engineers
Kubernetes core concepts and architecture (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 22)