Snowflake has announced new advancements aimed at enabling enterprises to deliver trustworthy artificial intelligence (AI) into production and enhance their data processing capabilities.
Enterprises are offered the ability to accelerate multimodal conversational application development with the introduction of more data sources and native agent-based orchestration. This enables data teams to construct cost-effective and high-performance natural language processing (NLP) pipelines with increased model choice, serverless large language model (LLM) fine-tuning, and provisioned throughput.
The AI Data Cloud company has integrated support for Container Runtime into Snowflake ML, allowing large-scale machine learning training and inference jobs to execute efficiently on distributed GPUs through Snowflake Notebooks. This integration enhances the development and deployment of conversational applications by harnessing both structured and unstructured data.
Baris Gultekin, Head of AI at Snowflake, highlighted, “For enterprises, AI hallucinations are simply unacceptable. Today’s organisations 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. 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.”
Thousands of global enterprises now use Cortex AI for scalable AI applications. The recent enhancements support enterprises in building conversational applications that use multimodal inputs like images, audio, and other data types. Additionally, new knowledge base connectors have been introduced, allowing users to integrate and manage internal knowledge bases swiftly. This is part of Snowflake’s effort to ensure its customers have access to comprehensive answers by using managed connectors such as the Snowflake Connector for SharePoint.
In terms of NLP, Snowflake has broadened its selection of pre-trained LLMs to provide organisations with increased choice and flexibility. This includes new inclusion of models from various providers, enhancing the processing and analysis of natural language data at scale. Snowflake is also introducing Cortex Playground, designed to facilitate model comparison and selection for specific use cases.
The company is enabling users to run large-scale ML training jobs on GPUs by supporting Container Runtime on AWS and Microsoft Azure. This support is accessible via Snowflake Notebooks and benefits users needing to develop ML models at scale.
Andrew Christensen, Data Scientist at CHG Healthcare, shared the benefits his organisation has experienced, saying, “As an organisation 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.”
Further customer testimonials underscore the impact of Snowflake’s capabilities across varied industries. These include Alberta Health Services, Bayer, and Coda, each of which has seen enhancements in AI app development, data processing, and resource efficiency.
Alberta Health Services, for instance, has explored new ways to automate physician note-taking through Cortex AI, revolutionising patient interaction recording and transcription. Jason Scarlett, Executive Director at Alberta Health Services, stated, “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.”
Overall, Snowflake continues to integrate advanced AI capabilities, providing enterprises the tools necessary for efficient and reliable AI deployment and data management, thus catering to a variety of sectors globally.
