How to build and deploy a real-world ML System in just 3 hours that gets you noticed by recruiters
(Works even if you NEVER learned MLOps, deployed zero ML models & stuck in Notebooks)

Did you know that 88.1% of entry-level candidates get ignored because they don't have experience in end-to-end ML?

Which means....
1. You spend weeks filling out online applications
2. Getting no calls back
3. Watching other people getting hired
4. Panicking that you'll never break into ML
Luckily for you, there is now a solution
Introducing "ML Project Blueprint".
The framework that helps you start building end-to-end ML systems, even if you have no idea how to start.

What is "ML Project Blueprint?"
01
Complete workflow that builds an ML system from raw data to cloud deployment, which means:
1. You will understand how to transform Notebook code into a deployed ML solution running on the cloud in real-time.
2. You will fully understand every step of a real-world ML system development.

02
Complete Python code of the End-to-End ML System, which means:
1. You'll attract new job opportunities from today by adding it to your ML Portfolio
2. You can use this template to build at least 1-2 more ML Projects in just 1 week.
3. You'll learn Docker setup to finally understand containerization and get ahead of 95% of applicants.

03
3 hours of video lessons, which means:
1. You'll learn how to build this project from scratch, including Docker, User Interface, Hyperparameter Tuning, and more.
2. You can easily explain in interviews how you built this ML app.

04
Interactive dashboard, which means:
1. You can stand out in interviews by demonstrating your ML model effectively.
2. You'll learn how to create User Interfaces for ML apps

05
Professional GitHub Repo with Code & Project Description, which means:
1. You can get job opportunities from Day 1 by sharing it with companies and your network.
2. You'll understand how to make your code and project look professional.

06
Detailed Architecture Diagrams, which means:
1. You'll clearly understand the full ML architecture, including ML Pipelines and Docker containers.
2. You'll outshine 95% of candidates in interviews by explaining the ML System logic and architecture.

Hi, my name is Timur
I am a Principal Data Scientist & Hiring Manager with 8+ years in ML
- I built 10+ end-to-end ML Solutions that bring $100 mln/year value to my clients
- I am a World TOP 3 LinkedIn Expert in ML & DS
- I helped 100+ clients to grow ML careers & land ML jobs
- 5 years in DS Team Leading and Hiring
- 2 year of ML career consulting experience
- I have a PhD in Applied Machine Learning

This is what your peers say
🎁 Launch bonuses
(only 4 days left!)
Bonus 1 🎁
Step-by-step CV Building Guide for ML pros
→ Built from 1,000+ resume reviews and 25+ DS hires
→ Includes pro tips for both beginners and experienced data pros
→ Learn what to cut, what to keep, and how to frame your skills

Bonus 2 🎁
Step-by-step LinkedIn Optimization Guide
→ Learn how to stand out on LinkedIn from World TOP 3 ML Profile
→ Optimize your headline, summary & keywords to boost visibility
→ Learn how to position your skills to attract the right opportunities

Bonus 3 🎁
100 ML Interview Questions and Answers
→ Get exact questions you need to learn based on 20+ taken & 100+ conducted interviews

Frequently Asked Questions
Q1: Who is this ML Project Blueprint for?
Q2: What exactly do I get in this product?
Q3: Do I need to be ML, Python or Docker Expert to use this Blueprint?
Q4: What makes this different from free resources?
Q5: Do you guarantee 100% that I'll get interview calls?
Q6: Is there a refund policy?
Build real ML systems and start getting interview calls. Now.
This is what you get
- Complete workflow that builds an ML system from raw data to cloud deployment
-
Ready-to-run ML project that you can add to your portfolio today.
-
Easy to reuse template to build 1-2 portfolio ML projects in 1 week to outshine other candidates.
-
3 hours of detailed videos to learn the complete end-to-end ML system development process.
-
Interactive dashboard to demonstrate your ML model effectively and stand out in interviews.
-
Complete Docker setup to finally understand containerization and get ahead of 95% of applicants.
-
GitHub Repo with Code & Project Description, which means you don't spend time making your code and project look good.
-
Solution diagrams to present your project professionally on GitHub & Interviews.
