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Towards high-quality (maybe synthetic) datasets (Practical AI #290)
Oct 9, 2024
57:04
Snipd AI
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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Quick takeaways
- Effective data collaboration between domain experts and data scientists enhances understanding and improves AI model performance in specific contexts.
- Establishing a baseline for AI initiatives is crucial to evaluate model effectiveness and optimize strategies in real-world scenarios.
Deep dives
Introduction to Fly and Tigris
Fly is highlighted as a flexible platform for building applications, providing unique features such as global Anycast load balancing and zero configuration private networking. It partners with data storage solutions like Tigris, which offers S3-compatible object storage that is globally distributed without the need for a CDN setup. Tigris allows users to upload assets to regional buckets, making them instantly available and simplifying the management of permissions for object storage. The ease of setup with a single command to create a bucket exemplifies the simplicity that Fly aims to provide for developers.
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Discover
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