Changelog Master Feed Towards high-quality (maybe synthetic) datasets (Practical AI #290)
Oct 9, 2024
Ben Burtenshaw is a machine learning engineer at Argilla, focused on data collaboration tools, while David Berenstein is a developer advocate engineer at Hugging Face, enhancing data quality for AI. They discuss the critical role of data collaboration in AI, the iterative process of dataset curation, and the partnership between AI engineers and domain experts. The conversation also explores synthetic data generation, AI feedback mechanisms, and the innovative use of multimodal datasets, including practical applications in healthcare to improve model training.
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Modeling AI Problems
- Start by defining your AI problem in simple terms, outlining inputs and desired outputs.
- Collaborate with domain experts to refine problem definition and ensure clarity.
Practical Steps for Starting AI Projects
- Write down expected questions for your AI system, starting with a small set.
- Associate documents with these questions and test if a model can answer them.
Argilla's Diverse Use Cases
- Argilla supports diverse use cases, from traditional models to newer RAG workflows.
- Many companies combine rule-based systems, traditional ML, and LLMs.