Generative AI for Developers
A 3-month course for technical specialists focused on using Generative AI for development
Generative AI for Developers
ABOUT THE PROGRAM
Today, GenAI presents a window of opportunity for developers and engineers as businesses undergo transformative changes, with the demand for AI in development steadily increasing.
We designed this micro-master’s program for engineers, architects, and data scientists who recognize the impact of AI on development and business and seek to strengthen their skill set. Over the course of three months, you will progress step-by-step from ML models as proofs of concept to developing solutions based on LLM.
WHAT YOU WILL LEARN





PARTICIPANT REQUIREMENTS

English proficiency at B2 level or higher

2+ years of experience in IT

Basic knowledge of cloud computing and infrastructure

Intermediate proficiency in Python (or another programming language)

Experience with data transformation libraries (such as Pandas) is a plus
EDUCATIONAL MODULES
Module 1: Foundations of ML and MLOps
This module is dedicated to filling the gaps in the principles of GenAI, ML, MLOps, and LLMOps:
- ML basics from the top GenAI tools and platforms to MLOps and LLMOps
- data preparation for ML models
- key principles of testing for LLMOps
Module 2: From ML Model to AWS Cloud Deployment
During this module you'll focus on deploying a model into cloud storage:
- features and specifics of Amazon Web Services
- security aspects of AWS
- AWS cloud infrastructure and services for data management: Lambda, Kinesis, Glue, SageMaker, Bedrock
- key principles of database systems management: ACID, CAP, BASE, PIE
Module 3: Developing LLM-Based Solutions
This module is dedicated to developing your own solution based on LLM:
- NLP/NLG for solution of different business cases
- LLM applications frameworks: prompts, chains, question answering, and agents)
- Building GenAI applications for content analysis and generation with Streamlit and Gradio
CURATORS AND INSTRUCTORS


Artem Kobrin
Artem is a highly experienced Head of Cloud practice at Neurons Lab, with a decade of successful international experience in DevOps, security, and networking AI, IoT, and Data Analytics. Artem leverages his expertise in transformative cloud migration, Conversational AI, data platform construction, and Digital Twin solutions to work closely with customers to deliver secure and scalable solutions and applications on the cloud.


Rostyslav Myronenko
Solutions Architect at Booking.com (Amsterdam, the Netherlands) with about 12 years of experience in software engineering in different engineering roles, Solutions Architect since 2018. From Kharkiv, Ukraine.
Main focus: solution architecture, AWS, Kubernetes, SDLC, team leadership, mentoring, growing a team.
Full AWS Certified (13 active AWS certifications), holder of the legendary AWS Golden Jacket.


Oleksii Popov
CTO with 15+ years in engineering leadership and solution architecture. Experienced in leading global teams and large-scale projects, from software engineering to Head of Engineering roles. Over 8 years in solution architecture at EPAM, Ciklum, and Customertimes, designing scalable, cloud-native solutions and implementing innovative technologies.

Ihor Tanenkov
Machine Learning Consultant at GlobalLogic, Founder of EntroPi AI – Artificial Intelligence service and product company. Ihor has over 10 years of commercial experience in Computer Vision, Machine Learning and AI systems.
PROGRAM ADVANTAGES


Flexible learning format that can be balanced with a full-time job

Learning from expert practitioners with ongoing feedback and support

Final project: your own unique LLM solution
WHO IT'S FOR

ML Engineers with experience in building models

Data Scientists looking to enhance their skill set

Middle+ developers and architects

Cloud Engineers

Technical specialists aiming to transition into MLOps or AI development roles
Reviews
Oleksii Popov
ʼI‘m glad to share that I have successfully completed the Generative AI for Developers course
This journey has been an incredible learning experience, deepening my knowledge of Generative AI, LLM applications, and AI-powered development workflows. The course covered essential concepts and hands-on projects, allowing me to work with cutting-edge AI frameworks and best practices for real-world AI applications.
💡 Key Topics & Technologies Explored:
– Building Generative AI Applications (RAGs, Fine-tuning, APIs)
– AI Model Deployment & MLOps Best Practices
– Cloud Infrastructure for AI & Data Engineering
– Automated Testing for LLMOps
– Advanced Prompt Engineering & Model Evaluation
– AWS & Cloud-Based AI Services
Beyond just learning, I had the honor of contributing back as a guest lecturer in one of the course workshops, sharing insights and experiences with fellow learners! 🎤💡
A huge thank you to SET University, Artem Kobrin, Rostyslav Myronenko Heorhii Kalaichev and other instructors for designing such a fantastic program! Looking forward to applying this knowledge in future AI-driven projects.
Vadym Yemelianov
I’m satisfied with the course; it was quite informative. I had the opportunity to ask the instructors many questions on topics that interested me, which was probably the most important thing for me. The theoretical sessions were good, but the initial topics were very basic for beginners, in my opinion. Toward the end, though, there were truly challenging and interesting topics.
As for advice, I’d only say that before each lesson, it’s better to do some self-study—Google, read about the topic that will be covered. That way, the material will be much easier to remember, especially during the later lessons.
Lyudmyla Povetkina
I’ll start with a bit about myself. I work as a Data Engineer (5+ years). I have also worked with an ML engineers & Data Scientists team. But that’s basically all my “ML experience.” Then, when LLMs appeared, the company decided to develop expertise in AI. We tried to find an AI engineer, but ran into the issue that it wasn’t clear what knowledge and skills such a specialist should have. But it became apparent that there is a “classic” ML specialist and a specialist in LLMs. That’s how my deep dive into ML, including LLM, began. There’s a lot of information, but it needed to be consolidated somehow, and we needed examples of how different technologies could be applied. That’s why we found your course.
Overall impressions of the program
Overall, the course is great and definitely very useful. Of course, there were moments when not everything worked out for everyone (both attendees and instructors). But these moments showed how to quickly find and solve problems. For me personally, as someone not closely involved with AI, there was a lot of new information—I even had doubts that I would understand everything at all. However, the materials and workshops conducted by speakers with real project experience helped me sort through it all. I’d like to single out the final projects in particular; some of them were really impressive.
Main insights I gained during the training
Before the course, LLM-related projects seemed incredibly complex. After completing it, I can’t say everything is easy, but it has all become clear. AI projects stopped being a “black box.” The final projects demonstrated a very broad, and sometimes unexpected, spectrum of possibilities for using LLMs.
Advice for future course students
Don’t be afraid to ask questions. The worst question is the one you don’t ask. Present your own ideas and projects, even if they’re not perfect. Expert feedback will help you choose a path for improving your idea or the next step for your own project. Believe in yourselves—you can do it!
FAQ
Do I need to have good coding skills?
Yes, coding skills are necessary, otherwise, this course will be irrelevant for you. The main task of the course is to learn how to use GenAI technologies for development. A confident skill in Python (or confident skill in another programming language) will allow students to work effectively through the program and create their own solutions within the practical module. If you don’t have any development skills, stay tuned for our updates as we regularly launch relevant programs for non-technical specialists.
Will there be a final project as part of the course?
Yes, each homework will be a part of the final project, which is your own LLM solution (for example, an AI agent for a chatbot).
If I've already been working as an ML engineer or MLOps, will this program be useful for me?
Yes, the program will allow you to systematize your knowledge of working with Generative AI models and cloud technologies to deploy your own solutions.
Learn more about the SET University program
