Deploying ML Models on Kubernetes
From Docker Containers to Production-Ready Kubernetes Deployments (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 23)
100 Days of MLOps23
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 Kubernetes for MLOps Engineers
Kubernetes core concepts and architecture (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 22)
Week 3 Review: The Cloud ML Workflow, End-to-End
Consolidate everything from cloud storage through SageMaker training to a live inference endpoint, then put a price tag on running it in production. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 21)
SageMaker Model Deployment & Endpoints
Take your trained machine learning models from the registry to production by deploying scalable, secure, and highly available real-time inference endpoints with Amazon SageMaker. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 20)
Introduction to AWS SageMaker Training Jobs
Running ML training in the cloud with SageMaker. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 19)
Cloud Storage for ML: S3 and GCS Basics
Your models and datasets need a home that isn't your laptop. Today we set up S3 buckets, lock down access with IAM, move files with boto3, and connect DVC straight to the cloud. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 18)