<p>1. Abstract<br /> This course is designed for participants who would like to explore the world of machine learning (ML) and artificial intelligence (AI), as well as those concerned about the return on investment (ROI) of their AI projects.</p> <p>During the course, Marcin Szymaniuk will minimise the focus on technical details and examine the topics through the lens of business and management. Participants will explore the substantial opportunities that big data solutions, ML, and large language models (LLMs) bring to business applications. The course will not shy away from the complexities involved. These include making practical choices about use cases, fine-tuning models to suit specific organisational needs, being mindful of costs, and addressing maintenance concerns.</p> <p>2. Agenda<br /> Introduction to ML and AI</p> <ul> <li>The potential</li> <li>Overview of use cases</li> <li>Overview of existing AI methods</li> </ul> <p>Pragmatic approach</p> <ul> <li>Defining the scope and objectives</li> <li>To use ML or not to use ML: when is AI overkill?</li> <li>Choosing the right approach: data vs. algorithm vs. expectations</li> <li>Research and development (R&D) vs. engineering</li> <li>Done is better than perfect</li> <li>Pitfalls at various stages of a project</li> </ul> <p>Challenges within the organisation</p> <ul> <li>Where is the data?</li> <li>Who owns the data?</li> <li>Managing technical debt: balancing engineering insights with business objectives</li> <li>Data quality</li> <li>Data democratisation</li> <li>Data mesh: a comprehensive solution or just hype?</li> </ul> <p>Legal aspects and regulations</p> <ul> <li>The General Data Protection Regulation (GDPR) and the EU AI Act</li> <li>Data governance</li> <li>Model governance</li> <li>Model explainability</li> <li>Upcoming regulations: what to expect?</li> </ul> <p>AI in production</p> <ul> <li>Defining “in production”</li> <li>What can go wrong?</li> <li>What organisations need to prepare in order to be truly “in production”</li> <li>Defining responsibility for the model lifecycle</li> <li>Adaptability: change is inevitable</li> <li>Cloud services, graphics processing units (GPUs), and other resources: what organisations need and when</li> </ul> <p>Generative AI</p> <ul> <li>Overview of use cases</li> <li>A simple approach to using LLMs: knowledge search and prompt tuning</li> <li>More sophisticated techniques: vector embeddings, retrieval-augmented generation (RAG), and model fine-tuning</li> <li>Privacy considerations</li> <li>Cost, scalability, and ROI</li> <li>Generative AI in production: how is it different from traditional ML - and is it?</li> </ul>