Global data labeling company SnorkelAI launches advanced data center AI capabilities

Recently, SnorkelAI launched a series of new features on its AI data development platform Snorkel Flow, aiming to help enterprises accelerate the specialization of AI and machine learning models. These new capabilities can significantly reduce the time, cost and complexity of data preparation in the predictive and generative AI development lifecycle.

Picture source note: The picture is generated by AI, and the picture is authorized by the service provider Midjourney

In today's enterprises, having "AI-ready data" (data suitable for AI) is crucial. According to Gartner, AI-ready data doesn’t just mean that your data needs to represent a specific use case, it must also cover every pattern, error, anomaly, and unexpected situation in order to effectively train or run an AI model. Moreover, data preparation does not happen overnight, but needs to be done continuously.

The new version of Snorkel Flow provides enterprises with a powerful platform to implement and scale AI data development practices to accelerate the production delivery of highly accurate, specialized AI models.

Specifically, new features include LLM assessment tools, which allow users to conduct customized assessments for specific industry use cases, gain insights into model error types, and quickly intervene in data development to fix them. In addition, there is a RAG tuning workflow that improves retrieval accuracy through advanced document block processing, fine-tuning of embedding models, and document metadata extraction. These features can significantly reduce the development time required to improve the quality of AI assistant responses.

With the new Named Entity Recognition (NER) function for PDF files, users can extract information more easily and quickly by simply clicking text, drawing bounding boxes, specifying patterns, and prompting the underlying model. This flexibility makes information capture easier, thereby improving the accuracy of the NER model.

In addition, Snorkel Flow simplifies the annotation and feedback process, allowing experts to annotate data in a more efficient manner. In addition, the newly added sequence marker analysis tool can help users more intuitively discover errors in model predictions while providing more detailed performance analysis.

In terms of user experience, Snorkel Flow has made a series of optimizations to make collaboration between data scientists and experts smoother. It supports seamless integration with major AI development platforms, including Databricks and Amazon SageMaker, for faster fine-tuning and deployment of specialized models.

Alex Ratner, CEO of Snorkel AI, said: “AI has become a priority for every business leader, but ongoing and consistent AI development is still very tedious, costly and labor-intensive. Therefore, updates to these platforms are important to help enterprises Accelerating and optimizing the delivery of AI solutions is critical.”

Highlight:

New features: Snorkel Flow launches LLM evaluation tool and RAG tuning workflow to improve data preparation efficiency.

Convenient extraction: New named entity recognition makes extracting information from PDFs easier and faster.

? Optimized experience: The user experience is improved to promote efficient collaboration between data scientists and experts.