Virtualization Technology News and Information
Snorkel AI Launches Application Studio, the Fastest Way to Develop AI Applications, and Raises $35 Million in Growth Funding Led by Lightspeed Venture Partners

Snorkel AI announced Application Studio, a visual builder with templated solutions for common AI use cases based on best practices from hundreds of deployments and research at top academic institutions over the last six years. Application Studio is in preview and will be generally available later this year within Snorkel Flow, the first AI development platform to programmatically label data and train, deploy and analyze models iteratively. 

"Over the years we've heard a clear refrain from enterprises working to deploy AI: data is the blocker. In many settings today-for example, ones where privacy, expertise or speed are essential-even the largest organizations can't afford to manually label the volume of data that modern machine learning approaches require," said Alex Ratner, co-founder and CEO of Snorkel AI. "Snorkel Flow's programmatic approach to training data labeling and model development uniquely unlocks these use cases and a whole new way to rapidly and iteratively develop AI applications-which we're now excited to make increasingly templatized and fast to deploy with Application Studio." 

According to Cognilytica's "Data Preparation & Labeling for AI 2020 Report," 80 percent of AI development time is spent on gathering, organizing and labeling data manually which is used to train machine learning models. Hand-labeling is notoriously expensive and slow with limited ability for development teams to build, iterate, adapt or audit applications in a systematic and privacy-compliant manner. The training data bottleneck has made AI application development an impractical endeavor, and 87 percent of the data science projects never make it into production.

"Snorkel AI addresses key points of pain for enterprises that need to digitally transform their businesses with production ML. Their data teams struggle to build, train and deploy accurate models at scale because the coding is complex and data volumes keep rising. They need to optimize their use of existing code, accelerate model development and organize training data more efficiently. They also need to collaborate on a common platform that supports the full ML lifecycle," said Kevin Petrie, Vice President of Research at Eckerson Group. 

Snorkel Flow makes it possible for data scientists, developers and subject matter experts to rapidly create and manage training data, train custom machine learning models, analyze and iterate to systematically improve and adapt and deploy AI applications quickly. With Snorkel Flow, organizations have achieved state-of-the-art machine learning model accuracy in days and 10-100x reductions in development time. Customers include two of the three top US banks, global insurance, biotech and telecommunications providers and several government agencies.

With the introduction of Application Studio, Snorkel AI lets enterprises develop AI applications faster than ever before. Application Studio provides data scientists, developers and subject matter experts with:

  • Pre-built solution templates: Pre-built solution templates based on industry-specific use cases such as contract intelligence, news analytics and customer interaction routing or common AI tasks such as text and document classification, named entity recognition and information extraction, give enterprise data science teams a head start in developing their own applications. Packaged application-specific pre-processors, programmatic labeling templates, models and features make customizing applications as easy as dragging and dropping new logic into the application flow.
  • High-performance models: Enterprises can use their own private data, labeled programmatically, to train state-of-the-art, open source model libraries available in the platform. Programmatic labeling replaces weeks or months of costly hand-labeling yielding highly accurate model performance. 
  • Collaborative workflows: Application Studio allows for an intuitive decomposition of complex applications into modular parts so that data scientists, developers and domain experts can collaborate easily and efficiently.
  • Auditable and adaptable capabilities: With Application Studio, the entire pipeline from training datasets to user contributions is versioned and can easily be audited. With a few lines of code, the applications can adapt to new data or goals. Unlike other application platforms that rely on hand-labeled data, there is no need to start from scratch. 
  • Data privacy at enterprise scale: Data breach and bias risks are the largest blockers to applying machine learning to many problem domains and sectors. With Application Studio, training data labeling and management are not only kept in-house but also can be done without humans needing to view the majority of the data-setting a new high bar for practical, private machine learning.
Snorkel AI Raises a $35 Million Series B Growth Funding Round
Today Snorkel AI also announced $35 million in Series B funding, bringing the total raised to $50 million. This round was led by Lightspeed Venture Partners; previous investors Greylock, GV, In-Q-Tel and Nepenthe Capital and new investors Walden and funds and accounts managed by BlackRock also participated. The company will use the funding to continue scaling its world-class engineering team and bringing its technology to leading enterprises.

Ravi Mhatre, Partner at Lightspeed Venture Partners and Snorkel AI Board Member, said: "Enterprises are spending billions of dollars to put AI to use today, and the AI technology market is expected to hit the half a trillion dollar mark in a few years. Snorkel AI is solving one of the biggest problems in AI - the data. With Snorkel Flow, organizations of all sizes, including some of the world's most sophisticated ones, are applying AI to mission-critical challenges and building solutions previously not possible. The traction has been incredible and they're just getting started." 

Published Wednesday, April 07, 2021 11:50 AM by David Marshall
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