Anaconda Inc., a leading platform provider for data science, machine learning, and AI, announced a new integration with Snowflake Notebooks (public preview), a cell-based development interface integrated within Snowflake’s secure, scalable platform. Snowflake Notebooks provide a convenient, easy-to-use development interface for Python, SQL, and Markdown to accelerate development using Snowflake offerings, including Snowflake ML, Streamlit, and Snowflake Cortex AI. The integration brings Anaconda’s secure, efficient, and robust Python packages within Snowflake Notebooks directly to accelerate data science, machine learning and AI development.
This integration extends Anaconda deeper into the Snowflake AI Data Cloud, empowering users to keep their data and development workflows within Snowflake’s secure and scalable platform. Leveraging Anaconda’s curated package repository enables users to meet stringent security standards and manage package dependencies effortlessly. This integration helps data scientists to focus on building and deploying models, without the typical security concerns associated with open-source software.
“The integration of Snowflake Notebooks with Anaconda represents a significant step forward in our mission to democratize Python and enable users to perform data science and AI tasks more efficiently,” said Peter Wang, Co-founder and Chief AI & Innovation Officer, Anaconda. “By combining the robust capabilities of Anaconda with the innovative features of Snowflake Notebooks, we are empowering users to drive faster insights, streamline workflows, and unlock new opportunities for collaboration and innovation in their data science and AI projects all from the familiar Snowflake environment.”
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Enhanced Workflows and Collaboration
Announced in November 2023, Snowflake Notebooks provide an all-in-one environment where data engineers, analysts, and data scientists can explore, analyze, and visualize data using Python and SQL. With features like native Git integration and scheduling, users can collaborate on notebook files, manage version control, and schedule data processes, enhancing efficiency and consistency in data workflows.
Key features of Snowflake Notebooks include:
- AI/ML Development: Build end-to-end machine learning pipelines with ease using a curated set of ML packages, all within Snowflake Notebooks.
- Collaboration Tools: Use native Git integration to version-control and collaborate on notebook files, connecting platforms like GitHub, GitLab, Bitbucket, and Azure DevOps.
- Integrated Data Analysis: Manage your entire data workflow within a single, intuitive environment. Access Snowflake platform capabilities and data sets directly within your notebooks.
“With Snowflake Notebooks, we’ve simplified the experimentation process for developers to iterate on their machine learning pipelines, harness AI-powered editing features, and streamline data engineering workflows in a style that’s familiar, intuitive, and powerful,” said Amanda Kelly, Director of Streamlit and Product Experiences, Snowflake and Co-Founder and COO, Streamlit. “Through this integration with Anaconda, we’re going even further to accelerate data analysis, development, and collaboration for developers at the cutting edge of innovation.”
Source: GlobeNewsWire
