Kodekloud 100 Days of MLOps
Notes on how I completed(ing) the kodekloud 100 days of MLOps tasks
The KodeKloud 100 Days of MLOps Challenge is a 100% hands-on learning program designed to help engineers move Machine Learning models from isolated local notebooks into reliable, automated, and monitored production systems.
Instead of traditional video lectures or multiple-choice quizzes, the challenge drops you into interactive, pre-provisioned terminal environments to solve real-world tasks with instant automated validation.
So In Nutshell
- 100 Real World Tasks: One objective per day designed to simulate production engineering scenarios (e.g., setting up Python virtual environments, building training pipelines, configuring CI/CD).
- Zero Local Setup: Every interactive lab environment (interactive terminal/control plane host) is provisioned for you directly in the browser—no local dependency issues or cloud bills.
- Automated Validation: Tasks are graded by automated verification scripts. Either your environment and pipeline meet the exact specifications, or they don’t.
- Modern Tooling: You work with industry-standard MLOps tools, including DVC, MLflow, FastAPI, BentoML, Kubernetes, Argo Workflows, and Evidently AI.
And finally you’ll receive a badge to showcase in LinkedIn or Portfolio
And this article series is about I navigated through this.
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Read articleKodeKloud 100 Days of MLOps – Day 1: Setting Up a Standardized Python Environment
Step-by-step walkthrough of Day 1 task in KodeKloud 100 Days of MLOps challenge: setting up a virtual environment, installing ML dependencies, and generating requirements.txt on controlplane.
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