Snorkel AI announced a major update to its flagship data labeling, filtering, curation, and AI fine-tuning platform named Snorfel Flow. The latest updates aim to address one of the most pressing challenges for companies looking to develop and deploy AI: integrating enterprise data with AI models.
Snorkel Flow update streamlines the integration of vast amounts of enterprise data into AI models. The platform can now directly integrate with Google's Gemini 3, Meta's recently released Llama 3, and other models. This gives companies more flexibility in choosing the LLM that best suits their needs.
This upgrade also includes data source integration with Vertex AI, Databricks Unity Catalog, and Microsoft Azure Machine Learning to streamline access for data labeling. Additionally, Snorkel Flow now supports programmatic labeling of multimodal data such as text, images, and audio.
The snorkel flow is Released in March 2022, automated data labeling allows organizations to significantly accelerate the development and deployment of AI applications. The first version included features such as collaborative AI development and an integrated ML modeling suite.
Snorkel Flow's approach to enterprise data management is to programmatically label and iteratively improve the large amounts of data used to train AI models. Snorkel AI claims that the Snorkel Flow approach allows you to: Reduce the time and cost of labeling data by 10-100x..
The latest update builds on previous versions by providing a more streamlined workflow for managing the data labeling process. Users can now define labeling functions, manage data sources, and monitor label quality. These upgrades enable better resource utilization for preparing enterprise data for AI training.
“Companies quickly hit a wall in terms of what they can accomplish with off-the-shelf LLMs, but by tailoring LLMs to their unique data and use cases, the next wave of value can be unlocked. “We believe we will continue to grow,” said co-owner Alex Ratner. Founder and CEO of Snorkel AI.
Ratner continued, “As base LLMs become more popular, including powerful open source options like Llama 3, the speed and precision of continuously labeling and curation of data to fine-tune and adjust LLMs will increase. will be a key differentiating factor.”
Snorkel AI began in 2015 as a research project at the Stanford AI Institute. In 2019, the startup launched from stealth mode and announced that he received $3 million in seed money. By 2021, the research project has grown to several funding rounds; Worth an astonishing $1 billion. The startup partners with some of the world's biggest companies, including IBM, Apple, Intel, and Uber.
The company specializes in data labeling, data augmentation, and model training. Snorkel AI provides tools that allow users to create high-quality training datasets more efficiently than traditional manual labeling methods.
As companies continue to rapidly focus on AI, more companies are offering training data services. Snorkel AI faces competition from companies large and small. Key competitors in the data label market include CloudFactory, Labebox, and Scale AI.
The upgrade to Snorkel Flow comes at a time when companies are looking to leverage AI across a variety of data modalities. Whether structured or unstructured data, AI technology is being applied to extract useful insights that enhance business decisions. Snorkel Flow's simplified data labeling and integration with powerful AI models give businesses the tools to unlock new possibilities in AI technology.
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