- 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
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.
This press release features multimedia. View
the full release here:
https://www.businesswire.com/news/home/20241112275545/en/
Snowflake Expands Capabilities for
Enterprises to Deliver Trustworthy AI into Production (Graphic:
Business Wire)
“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.
© 2024 Snowflake Inc. All rights reserved. Snowflake, the
Snowflake logo, and all other Snowflake product, feature and
service names mentioned herein are registered trademarks or
trademarks of Snowflake Inc. in the United States and other
countries. All other brand names or logos mentioned or used herein
are for identification purposes only and may be the trademarks of
their respective holder(s). Snowflake may not be associated with,
or be sponsored or endorsed by, any such holder(s).
About Snowflake
Snowflake makes enterprise AI easy, efficient and trusted.
Thousands of companies around the globe, including hundreds of the
world’s largest, use Snowflake’s AI Data Cloud to share data, build
applications, and power their business with AI. The era of
enterprise AI is here. Learn more at snowflake.com (NYSE:
SNOW).
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version on businesswire.com: https://www.businesswire.com/news/home/20241112275545/en/
Kaitlyn Hopkins Senior Product PR Lead, Snowflake
press@snowflake.com
Snowflake (NYSE:SNOW)
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