Tools and first-week setup

The tools a MLOps intern uses and how to set up your workspace in week one.

These are the main tools for MLOps tasks. You do not need all of them on day one; the Level 1 tasks tell you when to install each one.

Set up your workspace

Create a free GitHub account if you do not have one, with a professional username, and set up your LinkedIn profile: every task is submitted with a LinkedIn post.
Create one repository for the internship, for example mlops-internship, with a README that says who you are and what the repo holds.
Keep one folder per task, named with the task number and title, so mentors find your work quickly.
Commit small changes every day with clear messages. Your daily work report uses these commits as proof.
Never commit passwords, API keys or personal data. Put secrets in a .env file and add it to .gitignore.

Task	What to do
What MLOps is	Explain the ML lifecycle (data, training, deployment, monitoring) and where MLOps fits, with a diagram.
Reproducible environment	Create a project with a pinned requirements file or conda environment and a README that anyone can follow.
Train a baseline model	Train a simple scikit-learn model on a public dataset and record its accuracy and training time.
Project structure	Organise your ML code into data, features, train and predict modules instead of one notebook.