Speakers

ML and DL basics, supervised, reinforcement and unsupervised learning. Key concepts of Machine Learning (ML) and Deep Learning (DL); Model training approaches: supervised learning, unsupervised learning, reinforcement learning; Examples of ML and DL applications.

Generative models basics. In this speech, we will explore the fundamentals of generative models, delving into their core concepts and how they create new data, such as images, text, and audio. We’ll highlight their connection to cutting-edge technologies like AI and deep learning, showcasing practical applications across various fields. Expect insights into the mechanics, benefits, and transformative potential of generative models in shaping innovation.

Google Cloud Platform Review. Tools and practices for working with Big Data.

"Neural networks. Transformers. Building models with Keras and PyTorch"
We are going to cover the main concepts in Deep Learning and Neural Networks and their differences compared to classic ML / Data Science. Understand what task can be solved using TensorFlow and PyTorch.

"MS Azure and Big Data in Azure."
This presentation dives into Microsoft Azure's comprehensive AI platform, empowering developers to build intelligent applications at scale. We'll explore how Azure AI simplifies your AI toolchain, fostering the creation, evaluation and deployment of cutting-edge solutions.

"Database basics, relational, non-relational, distributed databases, data warehouse, ETL, data workflows."
We'll run through the overview of the DB basics, ways to operate with, and main use cases. In a nutshell we will cover how relational databases organize structured data, how NoSQL databases handle unstructured data, and the benefits of distributed databases for scalability and fault tolerance. Going forward to data warehousing, extraction and transfer processes.

Project and data management (Git, Data Version Control (DVC), Data sources (Kaggle, etc.), ML Hubs (Hugging Face, etc.)). How to manage effective tools and approaches to project and data management? I will explain the basics of working with Git for code version control, using Data Version Control (DVC) to track changes in data and models, as well as an overview of popular data sources such as Kaggle, and platforms for sharing models and tools, such as Hugging Face. The material will help you understand how to organize work with projects and data, ensuring reproducibility and efficiency.

Database usage for Data Science, Data Analysis and Machine Learning. The presentation explores the role of databases in Data Science, Data Analysis, and Machine Learning workflows. Key topics include selecting appropriate database types (SQL, NoSQL, and graph databases) for different data scenarios, optimizing data retrieval and preprocessing for analysis, and integrating databases with machine learning pipelines. Real-world examples demonstrate how effective database management enhances data-driven insights and supports scalable machine learning applications.

NLP, NLP tasks, NLP libraries and frameworks (e.g. NLTK). Supervised Machine Learning Unsupervised Machine Learning Reinforcement Machine Learning Markiyan Fostiak Software Engineering Lead Cloud computing basics, SaaS, PaaS and IaaS. The presentation provides an introduction to cloud computing, focusing on its fundamental concepts and service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It highlights the key differences between these models, their use cases, and benefits for businesses and developers. Practical examples illustrate how cloud computing enhances scalability, flexibility, and cost-efficiency in modern IT environments.

This presentation delves into Amazon Web Services (AWS) and its capabilities for managing and analyzing big data. Key topics include AWS services such as S3, Redshift, EMR, and Glue, which facilitate data storage, processing, and analysis at scale. Practical use cases demonstrate how AWS empowers businesses to extract insights from large datasets, optimize workflows, and enable machine learning applications in a cost-effective and scalable manner.

LLM, Transformers, BERT, GPT models family, LLM fine-tuning techniques. The presentation explores Large Language Models (LLMs) and their underlying Transformer architecture, focusing on prominent models like BERT and the GPT family. It covers the evolution of these models, their capabilities in natural language processing tasks, and the core concepts behind their success. Additionally, fine-tuning techniques, including transfer learning, prompt engineering, and parameter-efficient tuning, are discussed to demonstrate how LLMs can be adapted for specific applications, enhancing performance and efficiency.

Why are soft skills critical for techies? We will discuss how to develop such key competencies as communication, collaboration and teamwork, time management, people skills and management.

This lecture covers modern DevSecOps implementation, beginning with the "shift left security" concept. We'll examine technical components including dependency scanning, automated security testing (SAST, DAST, IAST), secrets management, and Infrastructure as Code security. The practical segment focuses on organizational implementation: security champions programs, CI/CD pipeline security gates, and incident response protocols. We'll address GDPR and PSD2 compliance requirements, concluding with a live demonstration of key concepts. Learning outcome: Participants will gain practical knowledge and skills for implementing secure software development practices

Introduction to modern cryptography - where academia meets applied solutions. While implementing and working with modern cybersecurity solutions, one cannot omit one of the most powerful tools in terms of data protection - cryptography. This talk is dedicated to introducing and demonstrating modern cryptography as a broad spectrum of security tools commonly used to keep our data safe in the digital world. The lecture aims to summarize the knowledge and introduce the audience to the world of not only well-known cryptographic primitives but also less common ones such as Blind Signatures and Oblivious Pseudorandom Functions from a scientific perspective, along with examples of their use in real-world solutions.

Data Analysis Life Cycle (processing and visualization)
- Life Cycle Phases
- Data Collection and Preprocessing
- Data Visualization: Telling a Story with Data
- Tips and real-life examples (forecasting, Power BI vs.Excel, conditional formatting, segmentation, SQL query automation, naming, colours, calculation of parrots etc.)
- Simpler = Better

Ever wondered how your favourite apps stay functional 24/7, even while you’re catching up on sleep?
,br>In this lecture, we’ll delve into the world of background workers, focusing on .NET implementations and exploring their role in powering real-world applications. For instance, systems like LeoCard, the unified electronic ticketing system for public transport in Lviv, likely rely on background services to process transactions or queues, manage user data, and ensure seamless operations for thousands of daily commuters.
Key takeaways from this session:
• Understanding Background Workers: Grasp the fundamentals of background workers and their significance in application architecture.
• Real-World Applications: Explore practical use cases, including emails sending or financial transaction management.
• Designing Resilient Systems: Learn tips for creating scalable and efficient background tasks.
• Cross-Language Perspectives: While focusing on .NET examples, we’ll also examine how similar tasks are handled in other languages, such as Python, using tools like Celery.

LLM fine-tuning techniques (RAG і GenAI agents)

Deep learning for forecasting