Snowflake is hosting the Bangalore leg of its Data Cloud World Tour (DCWT) event today. Attendees will get to hear from fellow data, technology, and business leaders including Sridhar Ramaswamy, SVP of Snowflake and Co-Founder and CEO of Neeva, and Sanjay Deshmukh, Senior Regional Vice President, Snowflake India and ASEAN, about how the Data Cloud breaks down silos, enables powerful and secure AI and machine learning (ML), and delivers business value to customers. Furthermore, the event will dig deeper into Snowflake’s latest capabilities making it easier for organizations to do more with their data and simplify architectures, build without governance tradeoffs, and deliver and monetize leading applications at scale in Snowflake Marketplace. In particular, attendees will learn about new advancements in the rapidly evolving areas of streaming, support for open table formats, and generative AI.
Snowflake Drives Generative AI in the Data Cloud with New Innovations and Partnerships
Snowflake recently announced new innovations that extend data programmability for data scientists, data engineers, and application developers so they can build faster and more efficiently in the Data Cloud. With the launch of Snowpark Container Services (private preview), Snowflake is expanding the scope of Snowpark so developers can unlock broader infrastructure options such as accelerated computing with NVIDIA GPUs and AI software to run more workloads within Snowflake’s secure and governed platform without complexity, including a wider range of AI and ML models, APIs, internally-developed applications, and more. Using Snowpark Container Services, Snowflake customers also get access to an expansive catalog of third-party software and apps including large language models (LLMs), Notebooks, MLOps tools, and more within their account. In addition, Snowflake is simplifying and scaling how users develop, operationalize, and consume ML models, unveiling new innovations so more organizations can bring their data and ML models to life.
Snowpark Empowers Developers with Broader Programmability, Without Governance or Security Tradeoffs
Snowpark continues to serve as Snowflake’s secure deployment and processing of non-SQL code with various runtimes and libraries — expanding who can build and what gets built in the Data Cloud. It lets builders work with data more effectively in their programming languages and tools of choice, while providing organizations with the automation, governance, and security guarantees missing in legacy data lakes and big data environments. Since launching in June 2021, Snowpark has helped data engineers migrate pipelines and run them faster and more efficiently, enabled data scientists to build and train models, and unlocked Snowflake as a powerful platform for application development.
Snowpark Container Services further expands the scope of workloads that can be brought to customers’ data. It provides users with the flexibility to build in any programming language and deploy on broader infrastructure choices, including the NVIDIA AI platform for optimized acceleration, with the same ease of use, scalability, and unified governance of the Snowflake Data Cloud. In addition, Snowpark Container Services can be used as part of a Snowflake Native App (in development), enabling developers to distribute sophisticated apps that run entirely in their end-customer’s Snowflake account.Snowpark Container Services will also enable users to securely run leading third-party generative model providers like Reka directly within their Snowflake account, removing the need to expose proprietary data to accelerate innovation.
Snowflake has partnered with dozens of third-party software and application providers to deliver world-class products that can run within their end-customer’s Snowflake account using Snowpark Container Services.
Furthermore, NVIDIA and Snowflake are building transformative accelerated computing and software integrations for Snowpark Container Services. Yesterday, the companies announced a partnership that aims to make advanced generative AI capabilities available to enterprises everywhere.
The collaboration also brings NVIDIA AI Enterprise — the software pillar of the NVIDIA AI platform — to Snowpark Container Services, along with support for NVIDIA accelerated computing. NVIDIA AI Enterprise includes over 100 frameworks, pretrained models and development tools including PyTorch for training, NVIDIA RAPIDS for data science, and NVIDIA Triton Inference Server for production AI deployments.
Snowflake Helps Bring ML Models to Life, Delivers Improved Developer Experiences, and Expands Streaming Capabilities
To streamline and scale machine learning model operations (MLOps), Snowflake is announcing the new Snowpark Model Registry, a unified repository for organizations’ ML models. The registry enables users to centralize the publishing and discovery of models, further streamlining collaboration across data scientists and ML engineers to seamlessly deploy models into production.
Snowflake is also advancing its integration of Streamlit in Snowflake, empowering data scientists and other Python developers to increase the impact of their work by building apps that bridge the gap between data and business action. With Streamlit in Snowflake, builders can use familiar Python code to develop their apps, transforming an idea into an enterprise-ready app with just a few lines of code, and then quickly deploy and share these apps securely in the Data Cloud.
In addition, Snowflake is making development within its unified platform easier and more familiar through new capabilities including native Git integration (private preview) to support seamless CI/CD workflows, and native Command Line Interface (CLI) (private preview) for optimized development and testing within Snowflake. New innovations also make it easier and more cost effective for data engineers to work with low latency data, without having to stitch together solutions or build additional data pipelines. Snowflake is eliminating boundaries between batch and streaming pipelines with Snowpipe Streaming (general availability soon) and Dynamic Tables (public preview), delivering a simplified and cost effective solution for data engineers to ingest streaming data and easily build complex declarative pipelines.