Foundation models are a powerful tools in machine that enable us to solve a wide range of problems and create innovative solutions. These models are capable of understanding language, recognizing images, and tackling various tasks by leveraging the extensive knowledge they have acquired during their training phase. Built as large-scale neural networks, Foundation models are pre-trained on diverse datasets, allowing them to identify complex patterns and nuances in new, unseen data, ultimately generating human-like responses.

In this tutorial, we will explore the fundamentals of Foundation models, their significance, and practical applications using Python. You will gain an understanding of the technology behind these models and their potential impact across different domains. Through guided explanations, we will create their own tiny foundation model and learn effective techniques for prompting and guiding the model to produce a desired outputs. We will also discuss common challenges, deployment strategies, and post-deployment maintenance considerations. By the end of the tutorial, you will have a solid grasp of a foundation model’s lifecycle and the skills needed to apply these models to real-world problems. This session is suitable for individuals with varying levels of AI experience.