Snowflake announced new advancements to enable businesses to put reliable artificial intelligence (AI) into production and improve their data processing capabilities.
Enterprises have the opportunity to accelerate the development of multimodal conversational applications through the introduction of more data sources and native agent-based orchestration. This enables data teams to build cost-effective, high-performance natural language processing (NLP) pipelines with increased model choice, serverless extended language model (LLM) fine-tuning, and provisioned throughput.
The AI Data Cloud company has integrated Container Runtime support into Snowflake ML, enabling large-scale machine learning training and inference tasks to run efficiently on GPUs distributed via Snowflake Notebooks. This integration improves the development and deployment of conversational applications by leveraging both structured and unstructured data.
Baris Gultekin, Head of AI at Snowflake, emphasized: “For businesses, AI hallucinations are simply unacceptable. Today’s organizations need accurate and reliable AI in order to make effective decisions, and that starts with access to high-quality data from a variety of sources. to power AI models. The latest innovations from Snowflake Cortex AI and Snowflake ML enable data teams and developers to accelerate the implementation of trusted AI with their enterprise data, so they can build chatbots faster, improve costs and performance. of their AI initiatives and accelerate the development of ML.
Thousands of global enterprises now use Cortex AI for scalable AI applications. Recent improvements help businesses create conversational applications that use multimodal inputs such as images, audio, and other types of data. Additionally, new knowledge base connectors have been introduced, allowing users to quickly integrate and manage internal knowledge bases. This is part of Snowflake’s efforts to ensure its customers have access to comprehensive responses using managed connectors such as Snowflake Connector for SharePoint.
In terms of NLP, Snowflake has expanded its selection of pre-trained LLMs to provide organizations with increased choice and flexibility. This includes a new inclusion of models from various vendors, improving natural language data processing and analysis at scale. Snowflake also introduces Cortex Playground, designed to make it easier to compare and select models for specific use cases.
The company enables users to run ML training jobs at scale on GPUs by supporting Container Runtime on AWS and Microsoft Azure. This support is accessible through Snowflake Notebooks and benefits users who need to develop ML models at scale.
Andrew Christensen, Data Scientist at CHG Healthcare, shared the benefits his organization has experienced, saying, “As an organization connecting more than 700,000 healthcare professionals to hospitals across the United States, we rely on the machine learning to accelerate our ability to place medical providers in temporary positions and permanent jobs. Using Snowflake Notebooks GPUs on Container Runtime proved to be the most cost-effective solution for our machine learning needs, allowing us to achieve faster recruiting results with higher success rates.
Other customer stories highlight the impact of Snowflake’s capabilities across various industries. These include Alberta Health Services, Bayer and Coda, each of which saw improvements in AI application development, data processing and resource efficiency.
Alberta Health Services, for example, has explored new ways to automate physician note-taking with Cortex AI, revolutionizing the recording and transcription of patient interactions. Jason Scarlett, CEO of Alberta Health Services, said: “It is being used by a handful of emergency department doctors, who are reporting a 10 to 15 per cent increase in the number of patients seen per hour; this means we can create fewer patients. – crowded waiting rooms, huge reduction in paperwork for doctors and even better quality notes.
Overall, Snowflake continues to integrate advanced AI capabilities, providing businesses with the tools needed to efficiently and reliably deploy AI and manage data, thereby addressing a variety of industries across the world. global scale.
