DS313BKK
Engineering and MLOps practices for Modern AI
Faculty
Mikhail Rozhkov
Technical Product Manager at Nebius AI, Founder of Machine Learning REPA Community
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
This intensive three-week course provides hands-on experience in implementing and managing modern AI solutions using MLOps practices. Through three real-world projects—a batch prediction system, a real-time service, and a RAG application—students will learn essential engineering practices and MLOps tools.
The course follows a "practice-first" approach, where students first implement quick prototypes and then gradually enhance them with production-grade MLOps practices. Each project builds upon the skills learned in previous weeks, fostering a comprehensive understanding of different ML system patterns and their implementation requirements.
Learning highlights
- Master the practical implementation of different ML system patterns (batch, real-time, RAG).
- Gain hands-on experience with industry-standard MLOps tools and practices.
- Learn to identify and apply appropriate MLOps practices for different ML systems.
- Develop skills in building production-ready ML applications.
- Understand the trade-offs in ML system design and implementation.
Course outline
15 classes
MLOps Introduction & Quick Start
Set up project structure, create initial ML pipeline, implement basic prediction flow.
Data Pipeline & Experimentation
Implement data versioning, create feature pipeline, track initial experiments.
Pipeline Orchestration
Create DAGs for data and training pipelines, implement error handling.
Quality & Monitoring
Add data validation, set up monitoring dashboards, and implement tests.
Project Demo & MLOps Review
Group presentations of batch prediction systems, MLOps practices discussion.
Model Serving API
Create FastAPI service, implement endpoints, add model serving.
ChatBot Development
Build Telegram bot, implement async handlers, add error handling.
Service Optimisation
Implement caching, run load tests, optimise performance.
Production Deployment
Deploy service, add security measures, set up monitoring.
Project Demo & MLOps Review
Group presentations of real-time services, MLOps practices discussion.
RAG Pipeline Setup
Set up RAG pipeline, implement vector storage, add LLM integration.
Vector Store & Embeddings
Build content processing pipeline, implement embedding generation.
Evaluation and Tracing
Implement Evaluation and Tracing pipelines.
LLM Service Optimisation
Optimise response generation, implement quality checks, add tests.
Project Demo & MLOps Review
Group presentations of RAG applications, MLOps practices discussion.
Course materials
Media
Prerequisites
Python programming (intermediate level).
Basic understanding of ML concepts and common algorithms.
Experience with basic ML libraries (scikit-learn, pandas).
Git basics.
Methodology
The course will be delivered through a combination of lectures, group projects, and individual coding assignments. Each week will focus on specific themes and tools, with practical exercises to reinforce the theoretical concepts discussed.
Learning Format:
Lectures: Theoretical background and conceptual overviews. Demo: Practical implementation. Practice: Hands-on development and assignments.
Topics that are out of the scope of this course:
Cloud provider specifics. Advanced infrastructure (k8s, etc.). Large-scale data processing. Distributed pipelines and services (Ray, k8s, Celery…).
Grading
Dr Mikhail Rozhkov is a Technical Product Manager at Nebius.ai, where he leads the development of a full-stack AI platform for AI/ML development and MLOps. He has over eight years of experience in Data Science, Machine Learning, MLOps, and AI product management.
Mikhail earned his degree in Marketing and began learning Data Analysis and Python programming during his PhD research at The Hong Kong Polytechnic University. Over the years, he has participated in and managed multiple ML projects in roles such as Project Manager, Senior Data Scientist, and Head of Data Science. He has also authored online courses and workshops on Reproducible ML Experiments, Pipeline Automation, and MLOps, which have been completed by over 5,000 professionals since 2020.
See full profileApply for this course
Engineering and MLOps practices for Modern AI
by Mikhail Rozhkov
Total hours
45 Hours
Dates
Jun 09 - Jun 27, 2025
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.