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)
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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)
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)