Enabling Real-Time, AI, and ML in the Enterprise

The push for modernization is a broad—yet necessary—endeavor that consists of numerous applications, tools, approaches, and platforms. The necessity for this push is dictated by the allure of two technological areas: Increasing real-time analytics and AI and machine learning (ML) capabilities. Though highly sought-after, this sort of modernization is wrought with technical challenges that stand in the way of becoming a competitive, modern enterprise in today’s market.

Dan DeMers, CEO of Cinchy, and Frank Liu, head of AI and ML at Zilliz, joined DBTA’s webinar, Unlocking the Power of Real-Time, AI and ML, to guide viewers through strategies and technologies for successful modernization which ultimately enable organizations to cater to the needs of modern data while overcoming its various challenges.

DeMers began by stating an “inconvenient truth,” asserting that any AI initiatives will fail to scale if an enterprise doesn’t first solve for data collaboration. He argues that collaboration is the big shift toward enabling true enterprise agility, whether that be document, messaging, meeting, project, or data collaboration.

Data collaboration replaces data integration in the workflow, according to DeMers. While data integration requires multiple producers to work independently on local copies, data collaboration allows multiple producers to work collaboratively on a single copy.

Fortunately for viewers, Cinchy provides just that; the Cinchy Data Collaboration Platform liberates data from applications and allows enterprises to manage and control data as products, eradicating the need for future data integration.

The end result is a more agile data ecosystem, enabling real-time and AI and ML efforts to be that much closer to becoming a tangible reality.

Liu then directed the conversation toward vector databases, which are purpose-built to store, index, and query vector embeddings from unstructured data. Unstructured data, or any data that does not conform to a predefined data model, is everywhere, according to Liu. The necessity for vector databases, then, is an obvious conclusion, as many traditional databases are not built to manage vector embeddings.

Crucially, vector embeddings can establish similarity search, which can connect different embeddings of different data sources to form similarity results.

Liu explained that while vector databases have a range of use cases—from image similarity search to molecular similarity search, anomaly detection, and more—its use case for generative AI (genAI) is particularly pertinent.

Text search engines that help users find relevant information, such as the ever-popular ChatGPT, are entirely reliant on vector databases enriched with relevant documentation.

Liu posed an example: A company that has over 100,000 pages of proprietary documentation to enable their staff to service customers can result in search that is slow, inefficient, and lacks context. With the implementation of an internal chatbot with ChatGPT and a vector database full of that proprietary documentation, employees and customers can better access relevant data, forwarding positive business outcomes.

For an in-depth discussion of enabling real-time and AI and ML in the enterprise, featuring demos, use cases, and more, you can view an archived version of the webinar here.