Production ML (MLOps): How to Stop Running Models Manually and Build an Industrial Machine Learning Infrastructure

## Introduction: Why MLOps Is Not Just a Buzzword, but a Necessity If you've ever deployed a machine learning model into production, you know: 80% of the time is spent not on algorithms, but on infrastructure. According to an Algorithmia report (2022), 55% of ML projects never make it to production. The main reason is the gap between model development and its operation. This is where MLOps comes to the rescue—a discipline that combines DevOps, machine learning, and data engineering. The **Production ML (MLOps)** course on the asibiont.com platform is designed for those who want to systematically master deployment, monitoring, and scaling of models. In this article, I'll tell you what you'll learn, who the course is for, and how the training is structured. ## What Is Production ML (MLOps) and Why Study It MLOps is the practice of automating and monitoring all stages of the ML model lifecycle: from data collection to deployment and drift monitoring. Key tools include Kubeflow, MLflow, feature stores, A/B testing, and hyperparameter tuning. Without them, modern ML infrastructure is impossible. The course on asibiont.com is aimed at engineers and data scientists who already know how to build models but want to learn how to "package" and maintain them in production. The program covers: - **Feature stores** — centralized management of features (feature engineering). - **Model serving** — deploying models via REST API, gRPC, batch inference. - **A/B tests and experimentation platforms** — how to test hypotheses without risking production. - **ML pipelines** — automating training, validation, and deployment using Kubeflow Pipelines. - **Data drift monitoring** — detecting changes in data distribution and concept shifts. - **Hyperparameter tuning** — optimizing hyperparameters using Optuna, Hyperopt. - **Cost optimization** — managing cloud resource costs. ## What You Will Learn: Specific Skills After completing the course, you will be able to: | Skill | Description | Tools | |-------|----------|-------------| | Model Deployment | Deploying models in a production environment | Docker, Kubernetes, MLflow, Seldon Core | | Feature Management | Creating and using feature stores | Feast, Tecton | | Pipeline Automation | Building end-to-end ML pipelines | Kubeflow Pipelines, Airflow | | Monitoring and Debugging | Detecting data drift and model drift | Evidently, WhyLabs, Prometheus | | A/B Testing | Conducting experiments and analyzing results | Custom frameworks, MLflow Experiments | | Cost Optimization | Controlling infrastructure expenses | Cloud cost management tools | These skills are in demand in the market: according to LinkedIn, the number of job postings mentioning MLOps has grown by 60% over the past two years. Companies are looking for specialists who can not only train models but also integrate them into business processes. ## Who This Course Is For The course is designed for three categories: 1. **Data Scientists** — you build models but want to learn how to deploy and monitor them so you don't have to hand off code to engineers. 2. **ML Engineers** — you are responsible for infrastructure and want to master modern tools (Kubeflow, MLflow). 3. **DevOps Engineers** — you work with CI/CD and want to apply your skills to ML projects. To take the course, basic knowledge of Python and an understanding of machine learning fundamentals are sufficient. DevOps experience is not required—everything is explained from scratch. ## How Training Works on asibiont.com The asibiont.com platform uses AI generation for personalized lessons. This means the program adapts to your level and goals. Here's how it works: - You register for the course and take an introductory test. - The neural network analyzes your knowledge and creates an individual learning plan. - Each lesson is generated in text format—no videos, only structured material with code examples, diagrams, and links to documentation. - You can ask questions to the built-in AI assistant, which explains complex topics in simple language. - Access to materials is 24/7, so you can study at any time. Why is this effective? Traditional courses often offer a linear program where half the material you already know, and the other half is irrelevant. AI learning solves this problem: the neural network selects topics and examples based on your context. For example, if you are a DevOps engineer, the focus will be on pipelines and monitoring; if you are a data scientist, on feature stores and A/B tests. ## Why AI Learning Is Modern and Convenient In 2026, AI assistants have become an integral part of EdTech. On asibiont.com, the neural network not only generates lessons but also: - **Explains complex concepts** — for example, data drift can be explained through the metaphor of a "broken thermometer": the model stops working because the data has changed, and it hasn't adapted. - **Provides practical assignments** — you don't just read theory; you write code, deploy models, and set up monitoring. - **Answers questions** — if something is unclear, the AI assistant is ready to explain it differently. This approach speeds up learning by 2-3 times compared to self-study. You don't waste time searching for information in documentation—everything is gathered in one place. ## Conclusion: Time to Build Production-Ready ML Infrastructure MLOps is not a luxury but a necessity for any company that wants to get real value from machine learning. The **Production ML (MLOps)** course on asibiont.com provides practical skills with tools used by leading teams: Kubeflow, MLflow, Feast, Evidently, and others. If you want to learn how to deploy models, monitor them in production, and automate the entire lifecycle—this course is for you. Start learning today: [Production ML (MLOps)](https://asibiont.com/en/course/ml-production).

Production ML (MLOps): How to Stop Running Models Manually and Build an Industrial Machine Learning Infrastructure

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