Snorkel AI | Jumpstarting Data Centric AI

TL;DR

Snorkel Flow introduces a Foundation Model Management Suite that helps enterprises leverage large language models to automate tasks, improve accuracy, and accelerate AI development.

Transcript

foreign welcome to greymatter the podcast from Greylock where we share stories from company Builders and Business Leaders I'm Heather Mack head of editorial at Greylock today Greylock General partner Sam modimedi interviews Alex Ratner who is the CEO and co-founder of snorkel Sam and Alex discussed the rise of large language or Foundation models in... Read More

Key Insights

• 🏑 The shift from model-centric to data-centric AI development is an ongoing trend in the field.
• 🪡 Foundation models show great potential but need adaptation and deployment to be impactful in enterprise settings.
• 📣 Snorkel Flow's Foundation Model Management Suite fills the gap by providing tools to leverage foundation models in a data-centric workflow.
• 🧑‍🔬 Collaboration between data scientists and subject matter experts is crucial for successful AI adoption.

Questions & Answers

Q: What is the main focus of Snorkel Flow's Foundation Model Management Suite?

The Suite focuses on leveraging foundation models to accelerate data labeling, improve training data quality, and fine-tune models for specific use cases in enterprise settings.

Q: How does Snorkel Flow address the challenges of deploying foundation models in production?

Snorkel Flow provides features like warm start, prompt builder, and fine-tuning to assist with adapting and deploying foundation models. It enables efficient auto-labeling of data, iterative development, and the creation of smaller deployable models.

Q: What are the potential use cases for Snorkel Flow's Foundation Model Management Suite?

The Suite can be used in various use cases, such as extracting information from complex customer documents in anti-money laundering and know-your-customer processes. It also accelerates labeling and development for high-cardinality machine learning problems.

Q: How does Snorkel Flow address the challenges of explainability and model auditing?

Snorkel Flow focuses on fine-tuning foundation models for specific tasks, allowing for interpretability and guarantees of performance. It emphasizes a data-centric approach, enabling separate auditing, control, and de-biasing of the training data and models.

Summary

In this episode of the Greylock podcast, Sam Modimedi from Greylock interviews Alex Ratner, the CEO and co-founder of Snorkel, about the rise of large language or Foundation models in AI and the role snorkel plays in helping enterprise organizations adapt and deploy these models. They discuss the shift from model-centric to data-centric AI development and the challenges and opportunities presented by Foundation models. Snorkel is announcing a set of capabilities that enable enterprises to use Foundation models in a data-centric workflow, accelerating the development and deployment of AI models.

Questions & Answers

Q: What is the shift from model-centric to data-centric AI development?

The shift from model-centric to data-centric AI development focuses on the importance of data in training machine learning models. Previously, developers focused on building models with unique architectures and bespoke infrastructure, while the data used for training was seen as secondary. However, with the advancements in machine learning technologies, models have become more powerful, automated, and standardized. As a result, the focus has shifted towards the data and how it is labeled and curated to teach machine learning models effectively. This shift has become a core focus for enterprises.

Q: What are Foundation models?

Foundation models, also known as large language models, are big, self-supervised models that have gained significant attention in the AI community. These models, like GPT-3, are trained on massive amounts of data and can generate incredible outputs, such as text and images. However, the challenge lies in translating this capability into real-world production value. Currently, the connection between these models and practical enterprise automation is limited. Snorkel aims to bridge this gap by using data-centric AI development to leverage Foundation models and deploy them in production.

Q: How does Snorkel enable enterprises to leverage Foundation models?

Snorkel is announcing the Snorkel Flow Foundation Model Management suite, a set of capabilities embedded in their existing platform. These features allow enterprises to use Foundation models in a data-centric workflow and derive production value. The suite includes features like Foundation model warm start, prompt builder, and fine-tuner. The warm start capability helps jumpstart the labeling process by leveraging Foundation models to auto-label data. The prompt builder allows users to prompt Foundation models to auto-label data in targeted ways, while the fine-tuner helps train smaller models for deployment using fine-tuning techniques. These features enable enterprises to quickly and effectively use Foundation models to accelerate their AI development and deployment processes.

Q: Can you provide a use case where Snorkel's Foundation model capabilities have had an impact?

A top three US bank has used Snorkel's Foundation model capabilities in their anti-money laundering (AML) and know your customer (KYC) processes. They faced the challenge of tagging and extracting information from complex customer documents, which required extensive manual labeling and a significant amount of time. By leveraging Snorkel's warm start and prompt builder features, they were able to jumpstart the labeling process and achieve high accuracy on simpler tasks. Additionally, they used other approaches, including manual labeling with the help of subject matter experts, to improve accuracy on more complex tasks. This resulted in a massive acceleration of the data labeling process, reducing it from weeks to hours, and improving performance.

Q: What are the risks associated with using Foundation models, and how does Snorkel mitigate them?

Foundation models present challenges in terms of explainability, risk, governance, and bias. Currently, the field is still exploring these areas, and there is a need to develop frameworks and controls to effectively manage these risks. Snorkel addresses these concerns by integrating Foundation models into a data-centric workflow that can be separately audited, controlled, explained, and de-biased. By using Foundation models as part of the process, Snorkel enables enterprises to leverage their power while still fine-tuning and iterating to ensure high accuracy, interpretability, and compliance.

Takeaways

The rise of Foundation models presents a significant opportunity for enterprises to leverage the power of large language models in their AI development and deployment processes. However, there are challenges in adapting and deploying these models into production. Snorkel's data-centric approach and the Snorkel Flow Foundation Model Management suite provides a bridge between the potential of Foundation models and their practical use in enterprise workflows. By integrating Foundation models into a data-centric workflow, enterprises can accelerate the development and deployment of AI models while ensuring accuracy, interpretability, and compliance. This approach addresses the challenges around adaptation, deployment, explainability, risk, governance, and bias associated with Foundation models. Overall, Snorkel's capabilities enable enterprises to unlock the true value of Foundation models and drive AI impact across various industries.

Summary & Key Takeaways

• Snorkel Flow is addressing the shift from model-centric to data-centric AI development, focusing on the importance of data labeling and curation.

• The rise of foundation models has presented exciting possibilities, but there is a gap in deploying them effectively in enterprise settings.

• Snorkel Flow's Foundation Model Management Suite aims to bridge this gap by using foundation models to accelerate data labeling, improve training data quality, and fine-tune models for specific use cases.