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How to go from Notebooks to modeling and designing End-to-End ML Systems
3200+ students already enrolled.
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Finally understand how End-to-End ML Systems work
✓ Scared of interview questions about ML System Design?
✓ Confused how your Notebook code fit production ML systems?
✓ Talks about ML pipelines, Orchestration & Deployment make you anxious?
You might even think that you're not smart enough to understand how all these ML Systems work.
But the reality is that it's NOT complicated.
The problem is that most courses explain ML System design as if everyone is a DevOps Engineer.
Let aside explaining how Notebook code fits the entire system architecture.
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.
I know how hard it can be to go from Notebooks to building End-to-End ML Systems.
I created this ML System Modeling and Design crash course, so you could also understand how to design ML systems and how they work.
Step-by-step.
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 3,100+ Data Scientists who understood how to model and design ML systems with no prior MLOps skills
What if I don't like the course?
You will get a 100% refund.
Start Modeling and Designing Real-World ML Systems. Now.
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- Demand Forecasting
- Churn Prediction
- Fraud Detection
- Video Recommendations