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DeeperML - #dataiku
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@insidehpc.com
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NVIDIA and Dataiku are collaborating on the NVIDIA AI Data Platform reference design to support organizations' generative AI strategies by simplifying unstructured data storage and access. This collaboration aims to democratize analytics, models, and agents within enterprises by enabling more users to harness high-performance NVIDIA infrastructure for transformative innovation. As a validated component of the full-stack reference architecture, any agentic application developed in Dataiku will work on the latest NVIDIA-Certified Systems, including NVIDIA RTX PRO Server and NVIDIA HGX B200 systems. Dataiku will also work with NVIDIA on the NVIDIA AI Data Platform reference design, built to support organizations’ generative AI strategies by simplifying unstructured data storage and access.
DDN (DataDirect Networks) also announced its collaboration with NVIDIA on the NVIDIA AI Data Platform reference design. This collaboration aims to simplify how unstructured data is stored, accessed, and activated to support generative AI strategies. The DDN-NVIDIA offering combines DDN Infinia, an AI-native data platform, with NVIDIA NIM and NeMo Retriever microservices, NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, and NVIDIA Networking. This enables enterprises to deploy Retrieval-Augmented Generation (RAG) pipelines and intelligent AI applications grounded in their own proprietary data—securely, efficiently, and at scale. Starburst is also adding agentic AI capabilities to its platform, including a pre-built agent for insight exploration as well as tools and tech for building custom agents. These new agentic AI capabilities include Starburst AI Workflows, which includes a collection of capabilities, including vector-native AI search, AI SQL functions, and AI model access governance functions. The AI search functions include a built-in vector store that allows users to convert data into vector embeddings and then to search against them. Starburst is storing the vector embeddings in Apache Iceberg, which it has built its lakehouse around.
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staff@insideAI News
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Dataiku, an AI platform company, has launched AI Agents with Dataiku, offering capabilities to create and control AI agents at scale. This new development aims to deliver a novel class of AI applications powered by analytics, predictive models, and agents. The company emphasizes that these agents can be grounded in trusted data, embedded in operational workflows, connected to all AI inputs, and governed with the same rigor as any business-critical asset, ensuring enterprise-level control and reliability. Dataiku highlights that over 20% of their customers are already leveraging Dataiku to integrate GenAI into their business and data workflows, showcasing the platform's growing adoption.
Dataiku's approach seeks to address the challenges of uncontrolled agent proliferation within organizations, which can lead to inconsistent quality, lack of IT oversight, and ungoverned deployments across various teams. To counter this, Dataiku provides options for central agent creation, including a visual, no-code environment for business users and a full-code environment for developers. This dual approach ensures accessibility for users with varying technical skills while maintaining key capabilities to ensure the creation, connection, and control of AI agents at scale with confidence. The goal is to ensure that companies can move from exploration to operationalization of agents with centralized creation, optimized performance, and orchestration through existing IT assets. According to Florian Douetteau, co-founder and CEO of Dataiku, the time has come for companies to take control of AI's raw power. He believes that companies are on the cusp of repurposing existing enterprise applications built on systems like Snowflake, Workday, and SAP, by adding a new layer of AI-native applications. Dataiku's Universal AI Platform aims to meet the emerging market need by centralizing agent creation for governance, continuously optimizing performance, and fully orchestrating agents through existing IT assets. The company is focused on building the required capabilities within its platform to facilitate this transition.
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marie.duvignaux@dataiku.com (Marie@The Dataiku Blog
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AI agents are increasingly being adopted by businesses to streamline workflows and automate processes, according to recent reports from Dataiku and LlamaIndex. Dataiku highlighted tools and processes for building AI agents, which can automate tasks like report generation and information synthesis, freeing up knowledge workers to focus on more strategic initiatives. A demonstration showcased how these agents could pull answers from a knowledge base, escalate unanswered questions by creating support tickets, and even draft answers for support agents, significantly reducing the time spent by subject matter experts.
LlamaIndex offers tools to enhance document workflows, particularly in areas like legal reviews and invoice processing. They announced their LlamaCloud General Availability, accompanied by a $19 million series A funding round. A tutorial also covered the building of an agentic reasoning system for search and retrieval, known as corrective RAG, using LlamaIndex workflows.
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