How to Teach Your Own AI Model, Part 2: What Should You Build First? @CallstackEngineers
How to Teach Your Own AI Model, Part 2: What Should You Build First?  @CallstackEngineers
Uploaded July 2026 | Updated September 2026, 2 days ago
How do you move from using AI models to training one for a specific task?

In Part 2 of ‘How to Teach Your Own AI Model’, Kewin and Piotr explore what to build first, how to prepare the data, and how to start experimenting with model training on accessible hardware.

They cover:
🔹 Why quantization is making capable models smaller, faster, and easier to run locally
🔹 How to choose a focused first project across computer vision, NLP, audio, video, or structured data
🔹 Data annotation and preparation with Label Studio
🔹 Fine-tuning LLMs with tools such as Unsloth Studio, and the PyTorch and Transformers foundations beneath them
🔹 Where synthetic data fits, and why dataset quality and human expertise still matter
🔹 A practical learning path: understand the basics, run a model locally, and build small projects

Watch the full session for the live demos, tool walkthroughs, and a grounded discussion of how developers can build a real understanding of AI.

See you at other Callstack live streams and webinars ✨ callstack.com/webinars

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How to Teach Your Own AI Model, Part 2: What Should You Build First?

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