Daniel

In this short workshop, you'll get to fine-tune a language model on a custom dataset. We'll cover the main challenges and the building blocks of the fine-tuning procedure: model quantization, parameter-efficient fine-tuning (PEFT) and low-rank adapters (LoRA), chat templates and dataset formatting, and training arguments such as gradient checkpointing, gradient accumulation, sequence length, and optimizers. We'll use Google Colab, BitsAndBytes, and several Hugging Face libraries (peft, datasets, and transformers).

Biography:

Daniel Voigt Godoy is an Amazon best-selling author, solopreneur, data scientist, and teacher. He has self-published a series of technical books, "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide, which is used as textbooks in universities in the United States and Spain. His books were also translated to Simplified Chinese by China Machine Press.

He has been teaching machine learning, distributed computing technologies, time series, and large language models at Data Science Retreat, the longest-running Berlin-based bootcamp, since 2016, helping more than 200 students advance their careers.

Daniel is also the author of the edX course "PyTorch and Deep Learning for Decision Makers".

His professional background includes 25 years of experience working for companies in several industries: banking, government, fintech, retail, mobility, and edutech.

Vasanth

Biography:

Vasanth Rajendran is an engineering manager at Amazon focused on building large-scale AI systems for retail discovery and personalization. He leads cross-functional engineering and science teams that design and operate automated platforms for AI-driven content generation, scheduling, and continuous quality assurance, powering millions of customer journeys globally. His work spans distributed systems, production ML platforms, multi-model content generation pipelines, large-scale scheduling systems, and automated evaluation and QA frameworks. Vasanth is an active IEEE author and frequent speaker on production AI system design, with a strong focus on reliability, scalability, and real-world deployment.