Python Refresher for ML Engineering Tasks
Master functions, classes, decorators, and file I/O. Graduate from notebooks to production-grade ML scripts.
Sandip Das
Senior Cloud, DevOps, MLOps & ML Platform Engineer | Heading Cloud, DevOps & MLOps for start-ups | AWS Container Hero | Educator | Mentor | Teaching Cloud, DevOps & Programming in Simple Way
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Setting Up Your MLOps Development Environment
Before pipelines, registries, and Kubernetes, every MLOps engineer needs a clean, reproducible local setup. Today you'll install Python 3.10+, master pip and virtual environments, and configure VS Code as a full ML workbench.
Understanding MLOps and Its Core Purpose
Machine learning models are only valuable in production. MLOps is the discipline that gets them there — and keeps them working. Let's build the foundation.
217 hands-on Kubernetes labs are live — from your first Pod to MLOps, GitOps & SRE.
217 auto-verified labs across 11 tracks — Core, Networking, Security, Operators, Scaling, Observability, AI/MLOps and more.
Building an AI Security Governance Platform on AWS
A complete implementation guide covering least-privilege access enforcement, Amazon Bedrock security hardening, continuous IAM permission auditing, and automated compliance evidence generation — built for production at scale.
Spin up a real Kubernetes cluster in Kubernetes Labs — then learn it, hands-on.
The Kubernetes Playground creates real local kubernetes cluster using kind (pick your version, nodes & LoadBalancer), plus 30 guided, auto-verified labs from your first Pod to GitOps & Operators