Introduction: Why 87% of Models Never Reach Production
According to algorithmic reports from Google Research (2020), about 87% of machine learning projects remain at the Jupyter Notebook stage. The reason is not bad algorithms — the reason is the lack of MLOps practices. A Data Scientist can write excellent code on a local machine, but as soon as the model needs to be put into production, problems begin: data versioning, drift monitoring, A/B tests, scaling.
It is precisely to solve these problems that the Production ML (MLOps) course on the asibiont.com platform exists. This is not another lecture on how to install a library — it is a comprehensive guide to building production-ready ML infrastructure. Let's break down what you will learn and how the training is structured.
What is Production ML (MLOps) and Who Needs It
The course is designed for those who already know how to build models but want to learn how to deploy them into real business processes. If you are a Data Scientist tired of endless hyperparameter tuning in a vacuum, or an ML engineer who needs to organize a pipeline on dozens of servers — this course is for you.
Main topics of the course:
- Feature Stores: centralized storage and versioning of features
- Model Serving: deploying models via REST API, gRPC, batch inference
- A/B Tests: how to compare models in production
- ML Pipelines: automation of the entire cycle from data to monitoring
- Kubeflow and MLflow: practical work with tools
- Data Drift Monitoring: detecting concept drift and data drift
- Hyperparameter Tuning: automatic parameter selection
- Cost Optimization: how not to go broke on cloud computing
This is not academic theory — these are specific practices used at Yandex.Taxi, Booking.com, and Netflix (according to their tech blogs).
What You Will Learn: Skills That Will Immediately Be Useful at Work
After completing the course, you will be able to:
1. Build Reproducible ML Pipelines
Instead of running scripts manually, you will learn to automate the process through Kubeflow Pipelines. This means the model can be retrained on new data with one click, rather than spending a day running notebooks.
Example: Imagine you work in e-commerce and need to recalculate recommendations every night. Using pipelines, you set up a DAG (Directed Acyclic Graph) that automatically loads data, cleans it, trains the model, checks metrics, and deploys the new version. All of this is tracked in MLflow.
2. Monitor Data and Model Drift
The most common production problem is that the model starts to "dumb down" over time because data changes. The course teaches how to set up drift monitoring using libraries like Evidently AI (open source code available on GitHub). You will learn to determine when it's time to retrain the model, rather than guessing.
Real case: In one fintech startup, a credit scoring model started making mistakes 2 months after deployment due to changes in the economic situation. Without monitoring, this would have been noticed only after losses. With MLOps — they received an alert and retrained the model in an hour.
3. Optimize Infrastructure Costs
Cloud computing is expensive. You will learn how to use spot instances, choose the right machine types, and configure autoscaling. According to the experience of many companies, optimization can reduce costs by 2-3 times without losing performance.
4. Work with A/B Tests of Models
How do you know that a new model is better than the old one? It's not enough to simply compare accuracy on test data — you need to conduct an A/B test in production. The course teaches how to properly set up experiments, collect statistics, and interpret results.
How Training on Asibiont Works: AI Personalization
The asibiont.com platform uses a neural network to generate lessons. These are not recorded webinars — they are text materials that adapt to your level and goals.
How it works:
1. You register and indicate your experience (beginner/advanced)
2. AI selects the program: if you already know Python, basic lessons are skipped
3. During training, the neural network explains complex terms in simple language — for example, "data drift" is explained using the example of changing seasons in a clothing store
4. You ask questions, and AI answers immediately, without waiting for a teacher's review
5. Access to materials — 24/7 from any device
This is modern and effective: research (e.g., EdTech report 2025) shows that personalized learning increases material retention by 40% compared to classic courses. You don't waste time on what you already know, but focus on gaps.
Why this is important for MLOps:
- If you are a Data Scientist, AI will give you more practice with Kubeflow, not basics of statistics
- If you are an engineer, you will dive deeper into cost optimization and monitoring
- No fixed schedule: learn at your own pace
Who This Course Is For
| Level | Who Needs It |
|---|---|
| Data Scientist (1+ year experience) | Want to deploy models in production, not just write code in Jupyter |
| ML Engineer | Need to systematize knowledge of pipelines and monitoring |
| DevOps / MLOps Engineer | Want to understand the specifics of ML infrastructure |
| Team Lead | Planning to implement MLOps in the team |
The course is not suitable for beginners in Python or ML — you need a basic understanding of machine learning and experience with libraries like scikit-learn or TensorFlow.
Conclusion: Time to Move from Experiments to Production
The world of machine learning is changing. If before it was enough to know how to build models, now employers are looking for specialists who can take a project to production. MLOps is not a trendy buzzword, but a necessity for anyone who wants their models to bring real business value.
The Production ML (MLOps) course on Asibiont provides exactly the skills that are in demand in the market. You will learn to build reliable pipelines, monitor drift, and optimize costs — all with a personalized approach from AI.
Don't put off until tomorrow what you can automate today. Go to the course page and start learning: Production ML (MLOps).
Comments