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Finally understand how to make your model out of Notebook to production
✓ Avoiding great job posts because it asks for MLOps?
✓ Feeling behind people who have MLOps skills?
✓ Going outside Notebook makes you anxious?
And every successful Data Scientist I know felt this too at some point of the career.
You might even think that you're not smart enough to build and deploy real production ML systems.
But the problem is NOT you.
It's the traditional ML and MLOps courses split:
- ML courses focus purely on model building
- MLOps courses go straight to Kubernetes and CI/CD
In the end, you're left with scattered knowledge and no motivation to learn because it feels impossible.
I'm Timur, a Principal Data Scientist with 8+ years of experience, ranked as the TOP 3 World DS/ML Educator on LinkedIn.
Over these years, I built 10+ end-to-end ML Solutions that bring $100 mln/year value to my clients.
To help Data Scientists to finally model, design and build ML Systems, I created a course bundle:
- ML System Modeling and Design
- ML Project Blueprint
It already helped 3600+ Data Scientists combined to overcome fear of MLOps and properly deploy their first end-to-end ML system.
Without any prior MLOps experience.
In just 3 hours, you can get your real-world ML system up and running and become confident that YOU CAN do it.
step-by-step
01
Complete workflow that builds an ML system from raw data to cloud deployment, which means:
02
Complete Python code of the End-to-End ML System, which means:
03
3 hours of video lessons, which means:
04
Interactive dashboard, which means:
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05
Professional GitHub Repo with Code and Project Description, which means:
06
Detailed Architecture Diagrams, which means:
Get everything you need to start designing ML Systems
1. 2h Video Breakdowns
Detailed explanation of each ML system design and modeling approach.
2. ML System Design Guidebook
Architectures, diagrams, and design logic.
3. 4 End-to-End Notebooks
Practical modeling examples.
4. Private Community
Find new ML friends and opportunities.
Model and Design These 4 Real-World ML Systems
Predict future demand to optimize inventory, logistics, and supply chain decisions.
Amazon
Walmart
UberIdentify customers at risk of leaving so you can intervene before they cancel.
Netflix
Spotify
SalesforceFlag suspicious transactions in real time before they are processed.
PayPal
Stripe
VisaServe personalized content, products, or ads ranked by predicted user preference.
YouTubeNetflix
InstagramLearn the exact MLOps concepts used in real-world ML systems
ML Pipelines
How to structure end-to-end training and inference pipelines that are modular, testable, and production-ready.
Feature Engineering
Engineer the right features for each problem: lag features for forecasting, engagement signals for churn, velocity and graph features for fraud.
Feature Stores
When you need a feature store — online vs offline stores — and when simpler dbt + Airflow setups are enough.
Model Registry & Experiment Tracking
Version, tag, and compare models using MLflow — with full metrics, artifacts, and lineage tracking.
ETL Pipeline Design
Pull and unify data from multiple sources — Snowflake, PostgreSQL, Salesforce, Kafka — into clean, reliable pipelines.
Orchestration
Schedule and trigger pipeline runs using Prefect or Airflow, with time-based, drift-based, and performance-based logic.
Batch vs Real-Time Inference
Know when to use daily batch scoring vs sub-100ms real-time inference — and how the system design changes for each.
Streaming Architecture
How Kafka and Spark Streaming power real-time ML systems for fraud and recommendations — and how it differs from batch ETL.
Model Selection by Problem Type
Choose the right model for each system: XGBoost, Prophet, DeepAR, two-tower models, isolation forests, and more.
Output Design & Actioning
Structure model outputs — churn scores, fraud flags, forecast tables, ranked lists — so they plug directly into CRMs, dashboards, and APIs.
Upgrade your essential MLOps skills in just 2 hours
1. Become confident in how ML systems are designed
2. Clearly understand how ML System parts are connected
3. Be able to explain your architectural decisions clearly
4. Confidently apply ML Systems thinking in your ML projects
Join 3600+ Data Scientists who have started building production ML systems
What if I don't like the courses?
You will get a 100% refund.
Frequently Asked Questions
- Q1: Who is this MLOps Courses Bundle for?
A: It’s for anyone trying understand how to build real-world ML systems and get higher paying ML roles
- Q2: Do I have a Lifetime Access?
A: Yes, you will get LIFETIME ACCESS, so you can take the course at any time at your own pace.
- Q3: Is there a refund policy?
A: Yes, if within 14 days you believe the product is not worth its price or doesn't help you to start building end-to-end ML systems, you will get a refund.
No Questions Asked. - Q4: What exactly do I get in this product?
A: You’ll get:
• 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. - Q5: Do I need to be ML, Python or Docker Expert to use this Blueprint?
A: No.
You do need to know some Python and basic ML principles.
But by watching the video lectures and with some support of ChatGPT, you will be able to understand how this project is built, EVEN IF you don't understand some part of the project. - Q6: What makes this different from free resources?
A: This system is built from:
• From 8+ years of ML experience
• 5 years of leading Data Scientists
• 3 years of teaching ML
• 2 years of ML Career consulting.
So, using this framework, you’re not guessing.
You get EXACTLY what you need to start building ML systems.
This will be a great foundation to learn how to build more complex ML systems as you grow you skills. - Q7: Do you guarantee 100% that I'll get interview calls?
A: I can't guarantee that because landing a job is a process that depends on many factors, such as your profile, network, etc.
But what I can promise is that it will significantly increase your chances of getting a job because:
1. You will understand how to build ML systems.
2. You will outshine 95% of other candidates.
3. You can confindently share this project with your network or LinkedIn connections, who can refer you to positions.
Build real ML systems and start getting interview calls. Now.
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- Demand Forecasting
- Churn Prediction
- Fraud Detection
- Video Recommendations
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