How to Teach Your Own AI Model, Part 2: What Should You Build First?
Featuring
This session brought Kewin and Piotr back together to explore the practical side of teaching your own AI model: choosing a project, preparing the data, training a model, and understanding the trade-offs behind the tools.
Start with a model you can understand and run
The conversation opened with the rapid progress in smaller, more efficient models. Piotr unpacked model quantization through recent examples, showing how ternary and binary representations can dramatically reduce a model's memory footprint while retaining much of its quality. The discussion focused on what this shift means in practice: increasingly capable models can run locally, including on consumer hardware and mobile devices.
Choose a focused problem and prepare the data
Training starts with a well-defined task and data that matches it. Kewin and Piotr discussed practical starter projects across computer vision, natural language processing, audio, video, and structured data. They also demonstrated how annotation tools such as Label Studio make the data-preparation process more tangible: labeling examples reveal what a model needs to learn and where a dataset may fall short.
Fine-tune without losing sight of the fundamentals
The stream covered accessible tools for experimenting with fine-tuning, including Unsloth Studio, while connecting them to the underlying libraries many developers will encounter, such as PyTorch and Transformers. A graphical interface can lower the barrier to experimentation, but understanding the model, the training data, and the parameters remains essential to evaluating the output.
Treat synthetic data with care
Synthetic data can extend a dataset, particularly when real examples are limited. The speakers also examined its limitations: errors in generated data can carry into later models, reducing quality over time. Their advice was clear: use well-described, high-quality datasets, and keep human expertise close to the data and evaluation process.
Learn by building
For developers who want to go deeper, Kewin and Piotr recommended starting with AI fundamentals, learning the basic mathematical concepts behind models, and then building small projects. Running a model locally, fine-tuning a focused use case, or creating a LoRA for image generation can turn abstract concepts into practical understanding.
