Roadmap to Senior ML Engineer

This plan helps you move from entry-level to senior ML engineer in 8 weeks, focusing on practical skills, deeper ML knowledge, and building a complete ML project.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Strengthen ML Fundamentals

Focus on sharpening core machine learning knowledge. Learn to explain and use key algorithms. Get hands-on with data and code regularly.

2 weeks

Explain common ML models simply
Clean and prepare real data sets
Measure ML model performance
Read and understand ML code

Data cleaning (remove missing values, normalization)
Model evaluation (accuracy, confusion matrix)
Explaining algorithms (decision tree, random forest)
Basic data visualizations (matplotlib, pandas)

Introductory Machine Learning Book
Kaggle Tutorials
Online Notebooks
Python ML Libraries Guide

Write a Jupyter notebook explaining 3 ML models with charts
Publish a short report comparing model performance on a new dataset

Build Robust ML Pipelines

Learn to take an ML project from data to deployment. Automate workflows and use version control. Practice clean, organized code and testing.

2 weeks

Structure reusable ML code
Use version control in projects
Write tests for ML steps
Document ML pipeline choices

Pipeline creation (scikit-learn Pipeline)
Code organization (modular scripts)
Version control (Git)
Testing (pytest for ML functions)

Pipeline Tutorials
Git and GitHub Basics
ML Code Testing Guide
Effective Python Practices

Publish a GitHub repo with a full ML pipeline script
Write and pass basic tests for model and data steps

Work on a Showcase ML Project

Develop one deep showcase project: a tabular data ML web app. Integrate real data, state routing, user forms, API, testing, and deploy online for review.

3 weeks

Build a complete ML web app
Implement input forms and predictions
Add state management to app
Test and deploy the project

Web frameworks (Streamlit or Flask)
API design (REST endpoints)
State management (handle user sessions)
App deployment (Heroku or Hugging Face Spaces)

ML Web App Tutorials
Streamlit/Flask Documentation
Deployment Guides
Testing Web Apps

Host a working ML app with live demo link
Automate project tests (API, forms) and add a CI badge
Write project documentation (README, user guide)

Lead, Review, and Communicate ML Work

Improve review, mentorship, and communication skills. Practice reviewing code, leading a discussion, and sharing work results simply with others.

1 weeks

Give and receive code reviews
Present project choices to team
Write clear project summaries

Code review (pull request feedback)
Presentation skills (walkthrough slides)
Technical writing (project summary docs)

Code Review Best Practices
Presentation Templates
Writing Clear Documentation

Complete at least 2 peer code reviews (written feedback)
Present project demo and summary to peers
Share project documentation with others

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	ML Fundamentals and Data Skills	Understanding basics is key before building advanced projects.	Read about decision trees and random forests, Clean a real dataset (pandas), Visualize model results (matplotlib), Explain one algorithm using a notebook	Notebook with cleaned data and model comparison, Short report explaining findings
2	Model Evaluation and Communication	Evaluating results and sharing findings helps solidify learning.	Measure model accuracy and errors, Write model evaluation scripts (scikit-learn), Summarize model strengths and weaknesses in writing	Script to compare two models on test data, Written summary of evaluation
3	Pipelines, Testing, and Version Control	Good projects are clean, tested, and reproducible.	Build a basic ML pipeline (scikit-learn Pipeline API), Setup a GitHub repo, Write at least two tests for key code parts (pytest), Document pipeline flow in README	GitHub repo with pipeline code and sample tests, CI workflow passing badge
4	End-to-End Project Design	Planning helps manage a bigger project.	Choose a public tabular dataset, Sketch project flow (input, state, output), Draft app requirements and project plan	Project plan and requirements doc, Draft project folder structure
5	App Routing, State, and Forms	Smooth user experience needs good state handling and forms.	Build basic ML web app with routes (Streamlit/Flask), Add user input form for predictions, Handle app state for different users	Web app demo with working input form, Guide to using the demo
6	Testing and Improving the App	Reliable apps need strong testing and error handling.	Write user scenario tests (pytest for forms and saving state), Test edge cases (invalid input, empty fields), Fix bugs and improve user instructions	Test suite with at least 3 test cases, Improved app ready for deployment
7	Deployment, Automation, and Docs	A senior engineer ships, automates, and documents work.	Deploy app to Heroku or Hugging Face Spaces, Add CI for automated tests (GitHub Actions), Write full project documentation	Live demo link, README with setup and usage
8	Peer Review and Presentation	Sharing and reviewing is part of real senior roles.	Review peer’s code and give feedback, Prepare project demo slides, Present project and answer questions	Feedback on two code reviews, Project demo shared with team