GEC NEWS WIREAt Snowflake Summit 2024, Snowflake unveiled new tools and innovations to accelerate the development of enterprise-grade pipelines, models, and applications. These enhancements simplify complexity, offering new developer tooling and native integrations to speed up development and empower efficient product shipping in the AI Data Cloud.
“Thousands of developers around the globe already rely on Snowflake as their go-to development platform. Our latest innovations continue to push the boundaries of what builders can create in the AI Data Cloud, bringing familiar, yet powerful experiences to their enterprise data where it already lives,” said Jeff Hollan, Head of Applications and Developer Platform, Snowflake.
Harness Snowflake’s Speed and Simplicity to Advance AI Development
Developers are regularly looking for ways to shorten the time from prototype to deployment, with Snowflake quickly becoming the platform for builders to create powerful data products. By harnessing the combination of Dynamic Tables and Snowpipe Streaming, users can unlock low-latency transformation pipelines to fuel AI and machine learning (ML) model development, all within the AI Data Cloud. Over 2,900 customers today are already running more than 200,000 Dynamic Tables1 to build and manage production-grade data pipelines, and that number continues to grow.
Snowflake is now arming developers with even more ways to accelerate their AI development directly on their data in the AI Data Cloud with Snowflake Notebooks (now public preview) natively integrated with the full breadth of the Snowflake platform including Snowpark ML, Streamlit, and Snowflake Cortex AI. Snowflake Notebooks provides a single, easy-to-use development interface for Python, SQL, and Markdown. Developers can also leverage Snowflake Notebooks to experiment and iterate on their ML pipelines, harness AI-powered editing features, simplify data engineering workflows, and more to unlock increased productivity and collaborative development.
“With Snowflake Notebooks, we can easily integrate our experiment tracking with Weights & Biases directly within notebooks,” said, Lukas Biewald, Co-founder and CEO, Weights & Biases. “Centralized, secure access to Snowflake data and compute lets you run data engineering and analysis alongside ML modeling in a notebook-style format that’s familiar, intuitive, and powerful. We’re excited to see how our customers use it to run experiments faster.”
Snowflake is also adding a Snowpark pandas API (now public preview), enabling Python developers to work with the same pandas syntax they know and love for even more advanced AI and pipeline development, while benefiting from Snowflake’s performance, scale, and governance for execution.
