DataStax and NVIDIA Join Forces to Accelerate Enterprise AI Development

DataStax Announces DataStax AI Platform Built with NVIDIA AI

Reduces AI Development Time by 60%; Handles AI Workloads 19x Faster;Enterprises Can Create AI That Increases In Accuracy As Customers Engage DataStax, a one-stop generative AI stack platform, announced on October 17 that the DataStax AI Platform, built with NVIDIA AI, would reduce the AI development time by 60%. Kari Briski, NVIDIA, Vice President AI Software states, "Enterprises are harnessing AI to drive digital transformation across industries. The DataStax AI Platform, built with NVIDIA AI, enables companies to create AI-ready databases and rapidly deploy tailored AI applications, unlocking new levels of customer value." The DataStax AI Platform, built with NVIDIA AI is one such platform that gives enterprises a holistic solution for all parts of the AI development and production life cycle including data ingestion and retrieval , application development, deployment and ongoing AI training. The platform integrates the DataStax AI platform with NVIDIA AI Enterprise software, helping enterprises to build AI applications that use companies' enterprise data and context.As a result , it makes it easier for enterprises to improve their models through self-learning and get more accurate with customer use. NVIDIA NeMo Customizer and NeMo Evaluator simplify training or fine-tuning LLMs, SLMs, embedding models, and reranking models. Meanwhile, DataStax's AI application platform gives developers the dynamic control of search and retrieval that is necessary to tailor GenAI to individual customers. "PhysicsWallah is democratizing education through GenAI-driven learning experiences for over 20 million students in India. The DataStax AI Platform, built with NVIDIA AI provides a real-time solution for PhysicsWallah to offer personalized, high-quality learning and accessibility at scale. This partnership enables the company to manage a 50x surge in traffic with zero downtime, serving millions of students, " adds Sandeep Varma, PhysicsWallah, Head of AI. DataStax and Data Management DataStax delivers a RAG-first developer experience, with first-class integrations into leading AI ecosystem partners, working with developers' existing stacks of choice.With DataStax, anyone can quickly build smart, high-growth AI applications at unlimited scale, on any cloud. Hundreds of the world's leading enterprises, including Audi, Bud Financial, Capital One, Skypoint, and many more rely on DataStax. The company delivers industry-leading vector search, flexible hybrid search, knowledge graph and graph RAG, real-time AI analytics, streaming, pub/sub, and a linearly scalable NoSQL store. Available in the cloud(DataStax Astra), or cloud-native self-managed software (DataStax Hyper-Converged Database). The DataStax AI Platform, built with NVIDIA AI, is for both cloud and self-managed environments. This gives enterprises the flexibility to deploy as they prefer. Cloud deployments can leverage their Amazon Web Services (AWS), Microsoft Azure, or Google Cloud environments. Many large enterprises need to run their AI applications in cloud-native self-managed data centers to fully control their technology stack. This holds value for heavily regulated industries like banks, insurance companies, and healthcare companies, which have often had issues with other AI tools that weren't built for enterprise scale or compliance needs. Chet Kapoor, Chairman & CEO, DataStax: "As companies strive to leverage AI, we're laser-focused on simplifying and accelerating the path to production to unlock innovation at scale." Kapoor also mentions that DataStax AI Platform will change the trajectory of enterprise AI and redefine customer experiences.

DataStax merges its data stack with Nvidia's development tools to simplify AI development and fine-tuning - SiliconANGLE

The database company DataStax Inc. is teaming up with Nvidia Corp. as it strives to become the data platform of choice for enterprises' artificial intelligence initiatives. In an announcement today, the company said it's integrating its AI capabilities with the Nvidia AI Enterprise platform. The company claims the new integrated tools offering, dubbed the "DataStax AI Platform, Built with Nvidia AI," can reduce development time of AI applications that leverage proprietary data by up to 60% in some cases. It provides everything developers need to fine-tune their models and improve the accuracy of their responses. DataStax said it's offering a complete solution for AI that covers everything from data ingestion and retrieval to application development and deployment, together with continuous training. The key components include DataStax's Langflow platform, which provides an open-source visual framework for building retrieval-augmented generation or RAG applications. The DataStax Langflow platform was launched earlier this year, after DataStax acquired the creator of the open-source Langflow project, called Logspace. DataStax also supplies its integrated Data Management tools, which encompass its flagship NoSQL database AstraDB with integrated vector search, hybrid search, knowledge graph, RAG, real-time analytics, streaming and other capabilities. DataStax became one of the first traditional database companies to add vector search functionality last year, enabling unstructured data to be stored as vector embeddings for easier retrieval by large language models. With that update, it paved the way for DataStax's RAGStack offering, which is an "out-of-the-box RAG solution." RAG is a key technique used in AI development that makes it possible to provide additional context to LLMs from outside data sources. It allows models to deliver more accurate query responses, improving the performance of generative AI applications. DataStax said AI demands extremely diverse kinds of data, and so an integrated platform that provides access to it all is preferable to bolting on different tools for vector search, knowledge graphs and so on. Meanwhile, the Nvidia AI Enterprise platform adds a host of other interesting capabilities for AI developers, including Nvidia's NeMo Retriever tool, which makes it easy to connect individual LLMs to very specific datasets, and NeMo Curator, a data curation tool for building large datasets for pre-training and fine-tuning models. Other components provided by Nvidia include the NeMo Customizer, which is a performant and scalable microservice that helps simplify model fine-tuning and alignment for domain-specific applications. The NeMo Evaluator aids development by automating the evaluation process to test the accuracy of fine-tuned AI applications, while NeMo Guardrails makes it possible to add safeguards and prevent toxic or biased outputs. Nvidia AI Enterprise also integrates multimodal PDF data extraction capabilities, providing a blueprint for ingesting unstructured data from PDF files, and NIM Agent Blueprints, which is a catalog of pre-trained and customizable AI workflows for creating and deploying AI applications. The DataStax AI Platform, Built with Nvidia, looks to be the complete package for AI developers, and companies will be hard-pressed to find a more comprehensive platform for building and deploying their AI models. Whether or not it's the best platform of its kind remains to be seen, but DataStax is boosting its chances of success by making it as flexible as possible. Enterprises can deploy the platform on any of the major public cloud platforms - Amazon Web Services, Microsoft Azure or Google Cloud - as well as on-premises environments, the company said. That last option makes it especially useful for enterprises in heavily regulated industries, such as insurance, finance and healthcare, the company said. The integration makes sense because a lot of customers are using both platforms anyway, the company added. It explained that one of the problems enterprises face when bolting together various disparate tools for AI is that things have a habit of breaking down. For instance, the online travel agency Priceline.com LLC was already using DataStax's AI offerings in combination with Nvidia's NeMo tools, and it was spending a lot of time on trying to make everything work smoothly. "It will greatly reduce AI development time," said Priceline Chief Technology Officer Angela McArthur. "Having them integrated will greatly reduce the complexity for companies like us." Constellation Research Inc. analyst Holger Mueller said the integrated offering is interesting because it brings together Nvidia's proven infrastructure with a reliable platform-as-a-service vendor in DataStax. "The partnership makes it clear that Nvidia has ambitions in software too and it will help the company in that regard," the analyst said. "It makes it much easier for joint customers to feed their data into Nvidia's software and hardware and get their generative AI apps up and running. Some companies might be concerned about the dependencies they're entering through this partnership, but most won't worry as they just want to build their first, AI-powered applications." DataStax says the integrated platform will also provide more accuracy, giving developers more dynamic control over the data they feed into each AI application so they can improve their responses. That's especially important because companies are increasingly trying to use generative AI to improve productivity, with things such as PDF-driven chatbots for customer service and AI-powered analytics tools for surfacing business insights. "The companies we're talking to see these use cases as laying the groundwork for what they really want to do," said DataStax Chief Executive Chet Kapoor. "They want to build 'transformational' AI projects that fundamentally transform how they operate and optimize for their customers."

DataStax looks to help enterprises stuck in AI 'development hell', with a little help from Nvidia

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DataStax has been steadily expanding its data platform in recent years to help meet the growing need of enterprise AI developers. Today the company is taking the next step forward with the launch of the DataStax AI Platform, Built with Nvidia AI. The new platform integrates DataStax's existing database technology including DataStax Astra for cloud native and the DataStax Hyper-Converged Database (HCD) for self-managed deployments. It also includes the company's Langflow technology which is used to help build out agentic AI workflows. The Nvidia enterprise AI components include technologies that will help to accelerate and improve organization's ability to rapidly build and deploy models. Among the Nvidia enterprise components in the stack are NeMo Retriever, NeMo Guardrails and NIM Agent Blueprints. According to DataStax the new platform can reduce AI development time by 60% and handle AI workloads 19 times faster than current solutions. "Time to production is one of the things we talk about, building these things takes a bunch of time," Ed Anuff, Chief Product Officer at DataStax told VentureBeat. "What we've seen has been that a lot of folks are stuck in development hell." How Langflow enables enterprises to benefit from agentic AI Langflow, DataStax's visual AI orchestration tool, plays a crucial role in the new AI platform. Langflow allows developers to visually construct AI workflows by dragging and dropping components onto a canvas. These components represent various DataStax and Nvidia capabilities, including data sources, AI models and processing steps. This visual approach significantly simplifies the process of building complex AI applications. "What Langflow allows us to do is surface all of the DataStax capabilities and APIs, as well as all of the Nvidia components and microservices as visual components that can be connected together and run in an interactive way," Anuff said. Langflow also is the critical technology that enables agentic AI to the new DataStax platform as well. According to Anuff, the platform facilitates the development of three main types of agents: Task-oriented agents: These agents can perform specific tasks on behalf of users. For example, in a travel application, an agent could assemble a vacation package based on user preferences. Automation agents: These agents operate behind the scenes, handling tasks without direct user interaction. They often involve APIs communicating with other APIs and agents, facilitating complex automated workflows. Multi-agent systems: This approach involves breaking down complex tasks into subtasks handled by specialized agents. What the Nvidia DataStax combination enables for enterprise AI The combination of the Nvidia capabilities with DataStax's data and Langflow will help enterprise AI users in a number of different ways, according to Anuff. He explained that the Nvidia integration will allow enterprise users to more easily invoke custom language models and embeddings through a standardized NIM microservices architecture. By using Nvidia's microservices, users can also tap into Nvidia's hardware and software capabilities to run these models efficiently. Guardrails support is another key addition that will help DataStax users to prevent unsafe content and model outputs. "The guardrails capability is one of the features that I think probably has the most developer and end user impact,"Anuff said. "Guardrails are basically a sidecar model, that is able to recognize and intercept unsafe content that is either coming from the user, ingestion or through, stuff retrieved from databases." The Nvidia integration also will help to enable continuous model improvement. Anuff explained that the NeMo Curator allows enterprise AI users to be able to determine additional content that can be used for fine tuning purposes. The overall impact of the integration is to help enterprises benefit from AI faster and in a cost efficient approach. Anuff noted that it's an approach that doesn't necessarily have to rely entirely on GPUs either. "The Nvidia enterprise stack actually is able to execute workloads on CPUs as well as GPUs," Anuff said. "GPUs will be faster and generally are going to be where you want to put these workloads, but if you want to offload some of the stuff to CPUs for cost savings in areas where, where it doesn't matter, it lets you do that as well."