The Snowflake Data Cloud has become a game-changer for businesses leveraging data analytics, but did you know it also packs a serious punch when it comes to Artificial Intelligence (AI)?
In this blog, we’ll explore Snowflake’s most compelling features that support artificial intelligence and investigate the future of AI with Snowflake.
What are Snowflake’s Most Powerful Features for AI?
As a data and AI consulting company, phData has worked with hundreds of companies across diverse verticals to help them achieve success with their data. Much of this work has been AI-focused with Snowflake. That being said, we’ve had an intimate look into which Snowflake features are driving AI use cases for our clients. Listed below are those features and our thoughts on why they’re powerful.
Scalability
Snowflake’s scalability plays a crucial role in supporting artificial intelligence by providing the ability to effortlessly scale computing resources in response to the dynamic demands of AI workloads.
As AI projects often involve large datasets and complex computations, Snowflake’s horizontally scalable architecture allows organizations to seamlessly increase or decrease computational power, ensuring optimal performance during tasks such as model training, inference, and data processing.
This scalability enhances the efficiency of AI workflows and enables organizations to handle growing data volumes and evolving computational requirements, ultimately facilitating the development and deployment of sophisticated AI solutions.
An excellent example of this would be a streaming video service that wants to provide personalized video recommendations. By utilizing Snowflake as its central repository for user data and integrating it with various machine-learning tools, the service would be able to store and analyze petabytes of data efficiently, providing accurate recommendations at scale.
Snowpark ML
Snowpark ML serves as the Python library and foundational framework for complete ML workflows within Snowflake, encompassing functionalities for both model development and operations. Leveraging Snowpark ML, users can employ well-known Python frameworks for preprocessing, feature engineering, and training tasks. This facilitates the deployment and administration of models entirely within Snowflake, eliminating the need for data transfers, silos, or governance compromises.
Snowpark ML Modeling
Snowpark ML Modeling facilitates data preprocessing, feature engineering, and model training within Snowflake, utilizing well-known machine learning frameworks like scikit-learn, XGBoost, and LightGBM. Additionally, this API incorporates a preprocessing module capable of leveraging compute resources from a Snowpark-optimized warehouse to deliver scalable data transformations.
Snowpark ML Operations
Snowpark ML Operations (MLOps), with its Snowpark ML model registry, acts as a counterpart to the Snowpark ML Development API. This registry facilitates the secure deployment and administration of models within Snowflake, accommodating models trained within and outside the Snowflake environment.
Snowpark ML Data Access
Snowpark ML Data Access offers efficient and straightforward methods for integrating data into your machine-learning pipelines. Furthermore, the FileSet API facilitates the seamless transfer of data into a Snowflake internal stage from either a query or Snowpark DataFrame, providing convenient functionalities for data manipulation and integration with libraries such as PyTorch or TensorFlow.
Lastly, a collection of framework connectors ensures optimized, secure, high-performance data provisioning tailored for PyTorch and TensorFlow frameworks in their native data loader formats.
Snowpark Container Services
Snowpark container services streamline the deployment, management, and scaling of containerized workloads like jobs, services, and service functions within Snowpark, leveraging Snowflake-managed infrastructure, including configurable hardware options like NVIDIA GPUs for optimal performance.
Developers can customize Large Language Model (LLM) applications directly within Snowflake without data movement, deploying and fine-tuning open-source LLMs and vector databases with GPU infrastructure through the Snowpark Model Registry and integration with Snowflake Native Apps. This enables running sophisticated applications entirely within Snowflake, including notebooks and LLMOps tooling, for a seamless and secure development experience.
Additionally, Snowpark Container Services allows enterprise developers to create custom user interfaces for LLM applications using frameworks like ReactJS, deploying container images containing their code within Snowflake for tailored solutions within its ecosystem.
The capability to bring such powerful applications to compelling datasets within Snowflake is what we see as a driving force in the next generation of AI.
Cortex
Snowflake Cortex is Snowflake’s fully managed service for fast data analysis and AI development within its ecosystem, utilizing machine learning to provide automated predictions and insights. Cortex offers pre-built AI functions like sentiment analysis and text summarization accessible via SQL/Python queries or Snowsight interfaces, enabling easy data interpretation.
Cortex also supports the development of custom AI applications, including through Snowpark Container Services, facilitating flexible and scalable AI development directly within Snowflake. As it’s more accessible and lower risk than even utilizing Snowpark or Container Services, we see Cortex as the future of AI and ML in Snowflake, allowing more businesses to leverage AI with minimal work.
Snowflake Marketplace
The Snowflake Marketplace platform within the Snowflake Data Cloud allows users to discover, try, buy, and integrate third-party data and solutions directly into their Snowflake environment.
Users can explore a variety of data sets, connectors, and applications within the marketplace, acquire them seamlessly through Data Shares, and incorporate them into their Snowflake data workflows. This marketplace makes it easy to find and integrate external data sources, enhancing the versatility and capabilities of Snowflake and, ultimately, helping businesses get more value from their Snowflake investment.
This provides a unique opportunity for anyone creating an AI model using data in Snowflake. Many datasets are produced and sold on the Marketplace to enrich data models. This enables developers to retrieve data from industry experts or research institutions with valuable niche data relevant to their AI projects.
This can significantly improve the accuracy and applicability of their models.
Future of AI in Snowflake
Snowflake is bringing generative AI into data, empowering teams to maximize the value of the data by identifying the right data points, assets, and insights.
To continue this effort, Snowflake has recently acquired three companies that are helping bring advanced AI and deep learning to the Data Cloud:
Neeva
Neeva is a company founded to make the search even more intelligent at scale. Neeva created a unique and transformative search experience that leverages generative AI and other innovations to allow users to query and discover data in new ways.
Snowflake plans to infuse and leverage these innovations across the Data Cloud to benefit their customers, partners, and developers. Neeva also allows Snowflake to tap into some of the most cutting-edge search technologies available to bring search and conversation in Snowflake to a new level.
Streamlit
Streamlit is an open-source library that turns Python scripts into shareable web apps in minutes. No front-end experience is needed, and apps are written in pure Python. Over the past few years, Streamlit has become the standard for Python-based data app development, with 80 percent adoption in the Fortune 50 and hundreds of thousands of developers. Steamlit is a go-to platform to experiment and build LLM-powered, generative AI apps.
Applica
Applica is an AI platform for document understanding that Snowflake believes will further its customers’ ability to gain insights from unstructured data. With this acquisition, Snowflake’s customers can leverage unstructured data in the Snowflake Data Cloud more efficiently.
Closing
Artificial Intelligence has become more important than ever in the business world, and Snowflake has taken steps to ensure it’s leading the way. From its famous scalability to features like Snowpark and Cortex, Snowflake continues to improve itself to support the AI community.
For a limited time, we’re offering a free generative AI workshop to help explore the potential of AI. In a 90-minute session, we will:
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Explore use cases for generative AI within your business
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Evaluate enterprise readiness to execute on your top use cases
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Recommended next steps to advance AI for your organization
