Written by: Dwarak Sri, Bill Tennant and John Thuma
Generative AI has the ability to create new content, such as images, text, and even music, without explicit human input. This can have a wide range of applications, from creating personalized content for marketing purposes to aiding in scientific research and development. Generative AI can also help automate certain tasks, saving time and resources for businesses and organizations. Additionally, it can open up new creative possibilities and push the boundaries of what is possible in various industries. For these reasons and many others, BlueCloud has and is investing heavily in the application of Generative AI and its integration into Snowflake and other knowledge bases.
Why integrate Generative AI into Snowflake?
Investing in Snowflake and Generative AI is an easy decision. Snowflake is The Data Cloud, where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Thousands of customers across many industries, including 510 of the 2022 Forbes Global 2000 (G2K) as of July 31, 2022, use Snowflake Data Cloud to power their business. Snowflake has also been recognized as one of the leaders in the cloud-based data analytics market by industry analysts such as Gartner and Forrester. BlueCloud is an Elite Services Partner of Snowflake supporting customers’ use of the Data Cloud to unite siloed data, discover and securely share data, and execute diverse analytic workloads across multiple clouds and geographies.
It's about ease of use and democratizing insights:
The ability to access and analyze enterprise data using natural language queries provides a powerful tool for decision-making, data exploration, and improving operational efficiency across various sectors. But what are some of the advantages of Generative AI with Snowflake?
- Ease of Use: Natural language queries provide a user-friendly and intuitive way to interact with data. Users can simply type their questions in everyday language, eliminating the need for complex query languages or technical expertise. This promotes the dissemination of information to every level of the organization. Not just relational data, but tangible insights across all areas of internal and external information.
- Flexibility: This approach offers greater flexibility in formulating questions. Users can ask questions in various ways, rephrase them, and get instant responses. This flexibility allows for exploration and discovery of insights without being limited to predefined options or complex research into the data.
- Interactivity: Natural language queries enable interactive conversations with the model. Users can ask follow-up questions, seek clarifications, and refine their queries based on the responses received. This conversational aspect enhances the user experience and facilitates a deeper understanding of the data.
- Contextual Understanding: Custom OpenAI models trained on enterprise data can provide responses that consider the context of the conversation. It can remember previous queries and responses, allowing users to ask follow-up questions without explicitly restating the context. This contextual understanding enhances the efficiency of data exploration and analysis.
- Complex Queries: Natural language queries leveraging multiple data types expands knowledge accessibility within the organization. A formula vs a means to learn and adapt based on multiple layers of both complex information and data, as well as changes within the overall external environment.
Use Cases in Generative AI and Snowflake
Marketing: Personalization of customer experience
By leveraging OpenAI capabilities in combination with enterprise data, businesses can generate personalized content for their customers that takes into account their preferences, past behavior, and demographics. This enables the creation of targeted content that connects with their audience on a more personalized level that is supported by the factors contributing to higher levels of engagement and conversion rates, not simply the user’s bias toward specific causation and testing models.
Healthcare Providers
Applications powered by custom OpenAI models can assist with clinical decision support by providing real-time, evidence-based recommendations such as flagging potential drug interactions, suggesting treatment options for a specific condition, and providing relevant clinical guidelines. The development of the model to establish this recommendation engine is a complex process. Once completed, disseminating this information, and allowing non-technical users to interrogate the outcomes allows for rapid decision making and an overall increase in the probability of a decision backed by the right data.
Financial Services
Loan origination is a complex process that involves multiple steps, including collecting customer data, analyzing credit scores, assessing risks, and processing loan applications. By leveraging natural language processing (NLP) and machine learning (ML) capabilities of a custom OpenAI model trained on enterprise data, companies can automate many of these tasks. When customers apply for loans, the model can provide real-time guidance and support throughout the process.
How does the BlueCloud Generative AI integration work with Snowflake?
The BlueCloud Snowflake integration with Generative AI is powered by the Azure OpenAI API and uses Azure Cognitive Services for enriching all types of information to help identify and explore relevant content at scale. The tech stack uses cognitive skills for vision, language, and speech, and can use custom machine learning models trained on enterprise data from Snowflake and other data sources to uncover insights from all types of content. We also use the semantic search capability of Azure Cognitive Search, which uses advanced machine learning techniques to understand user intent and contextually rank the most relevant search results to provide a recommendation. The solution accepts user input in human language format and displays generated output rendered from queries against Snowflake data sources. The interactive UI built using Streamlit, a framework designed for creating ML and data science web applications, allows the user to ask follow-up questions and seek refined responses. The service can be easily adapted to specific use cases including Snowflake and other data sources and knowledge bases. The solution is appropriately called the BlueCloud GPT service and is available via a web interface for both technical and non-technical users within our customer base as a toolset to expedite and expand the advanced usage of internal and external data within Snowflake.
It even provides the ability to perform administrative functions in Snowflake without the user having to learn specific queries or code. Users can also review the generated SQL to ensure accuracy and overall traceability.
More about the BlueCloud Innovation Center
BlueCloud is different from other systems integrators! Not only does BlueCloud bring certified experts in Snowflake, AWS, Azure, and ThoughtSpot; we are making significant investments in Generative AI, vertical industry solutions, Snowflake Migration Accelerators, and other TCO/ROI calculators using Snowpark. Coupled with these investments and great people and process BlueCloud wants to bring innovation to its clients! Contact info@blue.cloud if you would like to learn more about these innovations or how we can support your initiatives. Keep an eye out for new roadmap features relating to the Generative AI Snowflake integration!
