Snowflake Expands Capabilities for Enterprises to Deliver Trustworthy AI into Production

• Enterprises can now further accelerate multimodal conversational app development with more data sources and native agent-based orchestration 
• Data teams can build more cost-effective, performant natural language processing pipelines with increased model choice, serverless LLM fine tuning, and provisioned throughput 
• Snowflake ML now supports Container Runtime, enabling users to efficiently execute large-scale ML training and inference jobs on distributed GPUs from Snowflake Notebooks

No-Headquarters/BOZEMAN, Mont. – November 12, 2024 – Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual developer conference, BUILD 2024, new advancements that accelerate the path for organizations to deliver easy, efficient, and trusted AI into production with their enterprise data. With Snowflake’s latest innovations, developers can effortlessly build conversational apps for structured and unstructured data with high accuracy, efficiently run batch large language model (LLM) inference for natural language processing (NLP) pipelines, and train custom models with GPU-powered containers — all with built-in governance, access controls, observability, and safety guardrails to help ensure AI security and trust remain at the forefront.

“For enterprises, AI hallucinations are simply unacceptable. Today’s organizations require accurate, trustworthy AI in order to drive effective decision-making, and this starts with access to high-quality data from diverse sources to power AI models,” said Baris Gultekin, Head of AI, Snowflake. "The latest innovations to Snowflake Cortex AI and Snowflake ML enable data teams and developers to accelerate the path to delivering trusted AI with their enterprise data, so they can build chatbots faster, improve the cost and performance of their AI initiatives, and accelerate ML development.”

Snowflake Enables Enterprises to Build High-Quality Conversational Apps, Faster
Thousands of global enterprises leverage Cortex AI to seamlessly scale and productionize their AI-powered apps, with adoption more than doubling¹ in just the past six months alone. Snowflake’s latest innovations make it easier for enterprises to build reliable AI apps with more diverse data sources, simplified orchestration, and built-in evaluation and monitoring — all from within Snowflake Cortex AI, Snowflake’s fully managed AI service that provides a suite of generative AI features. Snowflake’s advancements for end-to-end conversational app development enable customers to:

• Create More Engaging Responses with Multimodal Support: Organizations can now enhance their conversational apps with multimodal inputs like images, soon to be followed by audio and other data types, using multimodal LLMs such as Meta’s Llama 3.2 models with the new Cortex COMPLETE Multimodal Input Support (private preview soon).
• Gain Access to More Comprehensive Answers with New Knowledge Base Connectors: Users can quickly integrate internal knowledge bases using managed connectors such as the new Snowflake Connector for Microsoft SharePoint (now in public preview), so they can tap into their Microsoft 365 SharePoint files and documents, automatically ingesting files without having to manually preprocess documents. Snowflake is also helping enterprises chat with unstructured data from third parties — including news articles, research publications, scientific journals, textbooks, and more — using the new Cortex Knowledge Extensions on Snowflake Marketplace (now in private preview). This is the first and only third-party data integration for generative AI that respects the publishers’ intellectual property through isolation and clear attribution. It also creates a direct pathway to monetization for content providers. 
• Achieve Faster Data Readiness with Document Preprocessing Functions: Business analysts and data engineers can now easily preprocess data using short SQL functions to make PDFs and other documents AI-ready through the new PARSE_DOCUMENT (now in public preview) for layout-aware document text extraction and SPLIT_TEXT_RECURSIVE_CHARACTER (now in private preview) for text chunking functions in Cortex Search (now generally available).
• Reduce Manual Integration and Orchestration Work: To make it easier to receive and respond to questions grounded on enterprise data, developers can use the Cortex Chat API (public preview soon) to streamline the integration between the app front-end and Snowflake. The Cortex Chat API combines structured and unstructured data into a single REST API call, helping developers quickly create Retrieval-Augmented Generation (RAG) and agentic analytical apps with less effort. 
• Increase App Trustworthiness and Enhance Compliance Processes with Built-in Evaluation and Monitoring: Users can now evaluate and monitor their generative AI apps with over 20 metrics for relevance, groundedness, stereotype, and latency, both during development and while in production using AI Observability for LLM Apps (now in private preview) — with technology integrated from TruEra (acquired by Snowflake in May 2024).
• Unlock More Accurate Self-Serve Analytics: To help enterprises easily glean insights from their structured data with high accuracy, Snowflake is announcing several improvements to Cortex Analyst (in public preview), including simplified data analysis with advanced joins (now in public preview), increased user friendliness with multi-turn conversations (now in public preview), and more dynamic retrieval with a Cortex Search integration (now in public preview).
  

Snowflake Empowers Users to Run Cost-Effective Batch LLM Inference for Natural Language Processing

Batch inference allows businesses to process massive datasets with LLMs simultaneously, as opposed to the individual, one-by-one approach used for most conversational apps. In turn, NLP pipelines for batch data offer a structured approach to processing and analyzing various forms of natural language data, including text, speech, and more. To help enterprises with both, Snowflake is unveiling more customization options for large batch text processing, so data teams can build NLP pipelines with high processing speeds at scale, while optimizing for both cost and performance.

Snowflake is adding a broader selection of pre-trained LLMs, embedding model sizes, context window lengths, and supported languages to Cortex AI, providing organizations increased choice and flexibility when selecting which LLM to use, while maximizing performance and reducing cost. This includes adding the multi-lingual embedding model from Voyage, multimodal 3.1 and 3.2 models from Llama, and huge context window models from Jamba for serverless inference. To help organizations choose the best LLM for their specific use case, Snowflake is introducing Cortex Playground (now in public preview), an integrated chat interface designed to generate and compare responses from different LLMs so users can easily find the best model for their needs. 

When using an LLM for various tasks at scale, consistent outputs become even more crucial to effectively understand results. As a result, Snowflake is unveiling the new Cortex Serverless Fine-Tuning (generally available soon), allowing developers to customize models with proprietary data to generate results with more accurate outputs. For enterprises that need to process large inference jobs with guaranteed throughput, the new Provisioned Throughput (public preview soon) helps them successfully do so. 

Customers Can Now Expedite Reliable ML with GPU-Powered Notebooks and Enhanced Monitoring
Having easy access to scalable and accelerated compute significantly impacts how quickly teams can iterate and deploy models, especially when working with large datasets or using advanced deep learning frameworks. To support these compute-intensive workflows and speed up model development, Snowflake ML now supports Container Runtime (now in public preview on AWS and public preview soon on Microsoft Azure), enabling users to efficiently execute distributed ML training jobs on GPUs. Container Runtime is a fully managed container environment accessible through Snowflake Notebooks (now generally available) and preconfigured with access to distributed processing on both CPUs and GPUs. ML development teams can now build powerful ML models at scale, using any Python framework or language model of their choice, on top of their Snowflake data.

“As an organization connecting over 700,000 healthcare professionals to hospitals across the US, we rely on machine learning to accelerate our ability to place medical providers into temporary and permanent jobs. Using GPUs from Snowflake Notebooks on Container Runtime turned out to be the most cost-effective solution for our machine learning needs, enabling us to drive faster staffing results with higher success rates,” said Andrew Christensen, Data Scientist, CHG Healthcare. “We appreciate the ability to take advantage of Snowflake's parallel processing with any open source library in Snowflake ML, offering flexibility and improved efficiency for our workflows.”

Organizations also often require GPU compute for inference. As a result, Snowflake is providing customers with new Model Serving in Containers (now public preview on AWS). This enables teams to deploy both internally and externally-trained models, including open source LLMs and embedding models, from the Model Registry into Snowpark Container Services (now generally available on AWS and Microsoft Azure) for production using distributed CPUs or GPUs — without complex resource optimizations.

In addition, users can quickly detect model degradation in production with built-in monitoring with the new Observability for ML Models (now in public preview), which integrates technology from TruEra to monitor performance and various metrics for any model running inference in Snowflake. In turn, Snowflake’s new Model Explainability (now in public preview) allows users to easily compute Shapley values — a widely-used approach that helps explain how each feature is impacting the overall output of the model — for models logged in the Model Registry. Users can now understand exactly how a model is arriving at its final conclusion, and detect model weaknesses by noticing unintuitive behavior in production.

Supporting Customer Quotes:

• Alberta Health Services: “As Alberta’s largest integrated health system, our emergency rooms get nearly 2 million visits per year. Our physicians have always needed to manually type up patient notes after each visit, requiring them to spend lots of time on administrative work,” said Jason Scarlett, Executive Director, Enterprise Data Engineering, Data & Analytics, Alberta Health Services. “With Cortex AI, we are testing a new way to automate this process through an app that records patient interactions, transcribes, and then summarizes them, all within Snowflake’s protected perimeter. It’s being used by a handful of emergency department physicians, who are reporting a 10-15% increase in the number of patients seen per hour — that means we can create less-crowded waiting rooms, relief from overwhelming amounts of paperwork for doctors, and even better-quality notes.”  
• Bayer: “As one of the largest life sciences companies in the world, it’s critical that our AI systems consistently deliver accurate, trustworthy insights. This is exactly what Snowflake Cortex Analyst enables for us," said Mukesh Dubey, Product Management and Architecture Lead, Bayer. "Cortex Analyst provides high-quality responses to natural language queries on structured data, which our team now uses in an operationally sustainable way. What I’m most excited about is that we're just getting started, and we're looking forward to unlocking more value with Snowflake Cortex AI as we accelerate AI adoption across our business.”
• Coda: “Snowflake Cortex AI forms all the core building blocks of constructing a scalable, secure AI system,” said Shishir Mehrotra, Co-founder and CEO, Coda. “Coda Brain uses almost every component in this stack: The Cortex Search engine that can vectorize and index unstructured and structured data. Cortex Analyst, which can magically turn natural language queries into SQL. The Cortex LLMs that do everything from interpreting queries to reformatting responses into human-readable responses. And, of course, the underlying Snowflake data platform, which can scale and securely handle the huge volumes of data being pulled into Coda Brain.”
• Compare Club: “At Compare Club, our mission is to help Australian consumers make more informed purchasing decisions across insurance, energy, home loans, and more, making it easier and faster for customers to sign up for the right products and maximize their budgets,” said Ryan Newsome, Head of Data and Analytics, Compare Club. “Snowflake Cortex AI has been instrumental in enabling us to efficiently analyze and summarize hundreds of thousands of pages of call transcript data, providing our teams with deep insights into customer goals and behaviors. With Cortex AI, we can securely harness these insights to deliver more tailored, effective recommendations, enhancing our members' overall experience and ensuring long-term loyalty.”
• Markaaz: “Snowflake Cortex Search has transformed the way we handle unstructured data by providing our customers with up-to-date, real-time firmographic information. We needed a way to search through millions of records that update continuously, and Cortex Search makes this possible with its powerful hybrid search capabilities,” said Rustin Scott, VP of Data Technology, Markaaz. “Snowflake further helps us deliver high-quality search results seconds to minutes after ingestion, and powers research and generative AI applications allowing us and our customers to realize the potential of our comprehensive global datasets. With fully managed infrastructure and Snowflake-grade security, Cortex Search has become an indispensable tool in our enterprise AI toolkit."
• Osmose Utility Services: “Osmose exists to make the electric and communications grid as strong, safe, and resilient as the communities we serve,” said John Cancian, VP of Data Analytics, Osmose Utilities Services. "After establishing a standardized data and AI framework with Snowflake, we're now able to quickly deliver net-new use cases to end users in as little as two weeks. We've since deployed Document AI to extract unstructured data from over 100,000 pages of text from various contracts, making it accessible for our users to ask insightful questions with natural language using a Streamlit chatbot that leverages Cortex Search."
• Terakeet: “Snowflake Cortex AI has changed how we extract insights from our data at scale using the power of advanced LLMs, accelerating our critical marketing and sales workflows,” said Jennifer Brussow, Director of Data Science, Terakeet. “Our teams can now quickly and securely analyze massive data sets, unlocking strategic insights to better serve our clients and accurately estimate our total addressable market. We’ve reduced our processing times from 48 hours to just 45 minutes with the power of Snowflake’s new AI features. All of our marketing and sales operations teams are using Cortex AI to better serve clients and prospects.”
• TS Imagine: “We exclusively use Snowflake for our RAGs to power AI within our data management and customer service teams, which has been game changing. Now we can design something on a Thursday, and by Tuesday it’s in production,” said Thomas Bodenski, COO and Chief Data and Analytics Officer, TS Imagine. “For example, we replaced an error-prone, labor-intensive email sorting process to keep track of mission-critical updates from vendors with a RAG process powered by Cortex AI. This enables us to delete duplicates or non-relevant emails, and create, assign, prioritize, and schedule JIRA tickets, saving us over 4,000+ hours of manual work each year and nearly 30% on costs compared to our previous solution.”
  

Learn More: 

• Read more about how Snowflake is making it faster and easier to build and deploy generative AI applications on enterprise data in this blog post. 
• Learn how industry-leaders like Bayer and Siemens Energy use Cortex AI to increase revenue, improve productivity, and better serve end users in this Secrets of Gen AI Success eBook.
• Join us at Snowflake’s virtual RAG ’n’ Roll Hackathon where developers can get hands-on with Snowflake Cortex AI to build RAG apps. Register for the hackathon here. 
• Explore how users can easily harness the power of containers to run ML workloads at scale using CPUs or GPUs from Snowflake Notebooks in Container Runtime through this quickstart.
• See how users can quickly spin up a Snowflake Notebook and train an XGBoost model using GPUs in Container Runtime in this video.

• Check out all the innovations and announcements coming out of BUILD 2024 on Snowflake’s Newsroom.
• Stay on top of the latest news and announcements from Snowflake on LinkedIn and X. 

¹As of October 31, 2024.

Forward Looking Statements
This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake’s business strategy, (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake’s products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements.  As a result, you should not rely on any forward-looking statements as predictions of future events. 

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