DS413BKK
Building ML solutions: From System Design to Deployment
Faculty Profiles
Olga Filippova
Lead Data Scientist at X5 Group, Russia
Maxim Novikov
ML Engineer at Central University
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
This class will help you understand how complex ML systems are built and will be part of your portfolio that you can showcase and reason about. In three weeks, we're going to learn the principles of ML System Design and apply them to build an ML solution for a real-life problem. The problem will be detecting bots that pretend to be human on https://youare.bot/ platform. The problem is widely spread and is being solved in many social platforms and other places where communications between humans happen. We’ll deploy the bot classifier service and connect it to the real production system on https://youare.bot to get feedback, debug and improve our ML solution. To learn the problem from the other side of the fence, we’ll deploy our own bots designed to trick humans and classifiers. Eventually, we’ll have a hackathon to see who can design the best classifier and the best bot.
Learning highlights
- Learn ML System Design: work with real-world examples and create ML system design doc.
- Learn ML Engineering: work with popular open-source tools and create a production ML solution.
Course outline
15 classes
Intro
Course overview. What is ML System Design and how it differs from traditional system design. ML project lifecycle. What is a design document? Case-based discussion and problem solving.
Product design
Understanding a business problem. Collecting requirements.Metrics, Financial Impact. Offline and online metrics. Metrics pyramid. How to evaluate the financial effect of your project.
Solution Design
Collecting system requirements. Functional and non-functional requirements. Getting a top-level understanding of the solution architecture. Discussing examples.
Data Science Methodology
Formulating the ml problem, Feature Engineering, selection of ML metrics, selection of an ML algorithm, construction of validation. Discussing examples.
A/B Testing
Explore the purpose of A/B tests, key considerations for a successful launch, and the importance of validation using historical data. Understand scenarios where A/B testing is not feasible, gaining comprehensive skills to make data-driven decisions with confidence.
Deploying simple apps
Deploying a simple rule-based service in production (later you’ll replace rules with ML models). Quick SE recap: VS Code, bash, Git, dependency management (uv), code style and linting (ruff), ssh/scp, tmux, Docker, Docker Compose, also structured logs to make debugging and monitoring easier.
Easy UI for demo & testing
You’re going to learn Streamlit and Gradio and create simple UI applications that help demo your models, debug them, and run manual testing scenarios
Pretrained models FTW
We’ll select a pretrained model for our task and put it in production, replacing our rule-based service. You’ll evaluate accuracy, compare latency vs quality trade-offs, and run a lightweight error analysis to decide what to improve next.
LLMOps Essentials
Dive into LLMOps using hosted APIs (OpenRouter) or self-hosted open-source models (vLLM). We’ll cover key inference settings, cost/latency trade-offs, and basic security pitfalls with simple mitigations. You’ll deploy a bot on youare.bot to test the classifier and collect improvement cases.
Hackathon & Demo
You are going to talk to each other’s bots via youare.bot platform and see who has the most accurate classifier and the most convincing bot.
Mastering ML Experimentation
To train the model, you need to conduct many experiments and find what leads to quality improvement. You’ll explore the essentials of experiment tracking and model versioning using MLflow.
HTTP web services
You’ll learn how to serialize a model and make it available in production as a service or pipeline, including real-time HTTP serving. We’ll cover deployment patterns and tools such as FastAPI, MLflow Models (packaging/serving), and an overview of NVIDIA Triton.
Model Deployment
Running your models on your laptop is good for a demo, but not a production scenario. We’re going to deploy your service to a cloud VM (or fly.io / a similar platform) so it stays available for users. We’ll set up basic monitoring in Grafana and use structured application logs to debug incidents.
Hackathon
Time to combine all the parts of our solution together and test them in production. Once again, you are going to talk to each other’s bots via youare.bot platform and see who has the most accurate classifier, the most convincing bot, and who can distinguish bots from humans best.
Hackathon & Demo
The final test for our classifiers and bots. You’ll present what you did during these three weeks, and discuss the work on the project to find best practices and things to improve. We’ll also discuss how to make this project part of your portfolio, making these weeks beneficial not only for your skills, but also for your public profile and job opportunities.
Prerequisites
Machine Learning.
Deep Learning.
Software Engineering essentials: Git and Python, Docker.
Methodology
In the first week, we will explore ML system design through an overview of core concepts, system architectures, and real-world case studies from a range of machine learning applications.
The second week will focus on deploying simple ML solutions and integrating them into real systems with real users.
In the third week, we will delve deeper into production ML by collecting and labelling data, training models, and deploying our own ML solution. We will focus on improving model quality while ensuring robustness and service stability.
Grading
Awards
- 1 place
McKinsey WomenHack event, 2021
Olga Filippova is a Lead Data Scientist at X5 Group, one of the largest retailers in Russia. She has over six years of experience applying machine learning models to address business challenges. Previously, Olga worked as a team lead in the banking sector, implemented machine learning solutions for manufacturing, and participated in the development of a library for monitoring machine learning models at the startup Evidently AI.
She is a co-author and instructor of various machine learning courses, covering topics such as data science competitions, MLOps, classical machine learning, machine learning system design, and machine learning for managers.
See full profileMaksim Novikov is an ML Engineer at Central University, focusing on production-grade machine learning: workflow automation, MLOps pipelines, model deployment as scalable web services, and LLM-powered applications. He also teaches at the National Research Nuclear University MEPhI, where he has taught Mathematical Statistics and Neural Network Theory for several years. In addition, he contributes to the development of Central University’s Production ML course as a co-author.
Maksim began his career in microelectronics and microsystems software, working in research laboratories and testing environments. He later joined T-Bank, supporting the development of B2B products through analytics, experimentation, and data-informed product decisions.
See full profileApply for this course
Building ML solutions: From System Design to Deployment
by Olga Filippova, Maxim Novikov
Total hours
45 Hours
Dates
Jun 08 - Jun 26, 2026
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.