deepset Archives - AiThority https://aithority.com/tag/deepset/ Artificial Intelligence | News | Insights | AiThority Tue, 13 Aug 2024 10:03:48 +0000 en-US hourly 1 https://wordpress.org/?v=6.6.1 https://aithority.com/wp-content/uploads/2023/09/cropped-0-2951_aithority-logo-hd-png-download-removebg-preview-32x32.png deepset Archives - AiThority https://aithority.com/tag/deepset/ 32 32 deepset Launches Studio for LLM App Development with Cloud and NVIDIA AI Integrations https://aithority.com/machine-learning/deepset-launches-studio-for-llm-app-development-with-cloud-and-nvidia-ai-integrations/ Tue, 13 Aug 2024 10:03:48 +0000 https://aithority.com/?p=575119 deepset Launches Studio for LLM App Development with Cloud and NVIDIA AI Integrations.

deepset Studio empowers AI developers to design and visualize custom AI pipelines for deployment in mission-critical business applications deepset, the mission-critical AI company, today announced an expansion of its offerings with deepset Studio, an interactive tool that empowers product, engineering and data teams to visually architect custom AI pipelines that power agentic and advanced RAG […]

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deepset Launches Studio for LLM App Development with Cloud and NVIDIA AI Integrations.

deepset Studio empowers AI developers to design and visualize custom AI pipelines for deployment in mission-critical business applications

deepset, the mission-critical AI company, today announced an expansion of its offerings with deepset Studio, an interactive tool that empowers product, engineering and data teams to visually architect custom AI pipelines that power agentic and advanced RAG applications. AI teams are now able to more easily build top-tier composable AI systems, and immediately deploy them in cloud and on-premises environments using deepset Cloud and NVIDIA AI Enterprise software.

“Enterprises across industries are seeking ways to effectively integrate AI into their core operations while maintaining security and scalability”

“deepset is a leader in enabling custom AI development – powering many of the world’s most trusted, high-value use cases,” said Milos Rusic, CEO and co-founder of deepset. “The addition of deepset Studio now enables developers at the thousands of companies worldwide to architect the next generation of custom LLM applications. This new tool, combined with native integrations to NVIDIA AI Enterprise, provides a robust platform for enterprise developers to safely and reliably develop mission-critical generative AI products and features.”

Pushing the boundaries of customized AI-driven business applications

deepset Studio is a drag-and-drop visual environment for building customized AI pipelines. Its intuitive interface accelerates the AI development process with a user-friendly drag-and-drop UX for AI teams to architect a wide range of LLM use cases, from RAG to agentic applications. Key benefits of deepset Studio include the ability to:

  • Design AI pipelines with a drag-and-drop visual editor that automatically validates component relationships and pipeline structure.
  • Leverage Haystack’s comprehensive library of integrations and components to create flexible and composable application architectures like RAG and agents.
  • Jumpstart the development process with proven pipeline templates, component configurations, and shareable visual representations of simple to complex AI systems.
  • Go to production faster with native cloud and on-premises deployment options for deepset Cloud and NVIDIA AI Enterprise.

Extending the power of Haystack, deepset Cloud and NVIDIA

deepset Studio is available as a free standalone tool for users of the popular Haystack open-source framework and is built into the deepset Cloud platform and integrated with NVIDIA AI Enterprise for cloud or on-premises deployments of production AI. With this,

  • Haystack users can build and visualize AI pipelines for “cloud-to-ground” environments, speeding development time and simplifying collaboration. Haystack is the leading open-source AI framework for developing production-ready applications and is the choice for thousands of developers due to its quality codebase, flexible framework, and wide library of components and integrations.
  • deepset Cloud customers gain a powerful and intuitive visual editor as a platform feature to facilitate the creation of AI pipelines in the platform. deepset Cloud provides a complete set of development tools – from data management and LLM choice to prompt configuration and evaluation – empowering AI teams at companies such as Airbus and YPulse to develop and deploy enterprise-grade applications within a secure and scalable environment.
  • NVIDIA AI Enterprise users can optimize their deployments through deepset Studio’s integration with NVIDIA NIM microservices and the NVIDIA API catalog. Users can configure NIM microservices deployments and LLM inference directly in Studio. The tool provides deployment guides for setting up NIM and Haystack pipelines on Kubernetes, streamlining deployment to any cloud or data center.

What customers are saying:

“Enterprises across industries are seeking ways to effectively integrate AI into their core operations while maintaining security and scalability,” said Anne Hecht, senior director of product marketing for enterprise software at NVIDIA. “The integration of the NVIDIA AI Enterprise software suite with deepset Studio will help simplify and accelerate the deployment of AI applications, supporting both cloud and on-premises environments.”

“deepset is our trusted partner for launching high-quality AI applications quickly, said Dan Coates, President of YPulse. “Our customers love what we’ve built with deepset. We’re excited about deepset Studio, which simplifies AI development and showcases deepset’s rapid innovation. This tool allows us to visually transform AI ideas into customized product offerings with even more speed and ease as AI applications become increasingly sophisticated.”

Reserve a spot for deepset Studio beta

  • Sign up to use deepset Studio for free, unlocking an interactive visual environment to learn, explore, and build LLM applications with Haystack.
  • Developers can accelerate AI deployments with NVIDIA NIM microservices, available for free on the NVIDIA API catalog.
  • deepset Cloud customers have access to the Studio functionality in Beta now

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Industry-First Observability Features Elevate Trust in Generative AI Applications https://aithority.com/machine-learning/industry-first-observability-features-elevate-trust-in-generative-ai-applications/ Thu, 18 Jan 2024 11:05:54 +0000 https://aithority.com/?p=558089 Industry-First Observability Features Elevate Trust in Generative AI Applications

deepset Cloud’s Cost-Crushing Dashboard Shows What Works; Where Others Fall Short deepset’s Industry-First Observability Features Elevate Trust in Generative AI Applications  deepset Cloud became the first large language model (LLM) platform to provide insights into the precision and fidelity of responses from LLM generative AI through a first-of-its-kind “Groundedness Observability Dashboard.” With this 01/2024 release, the […]

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Industry-First Observability Features Elevate Trust in Generative AI Applications

deepset Cloud’s Cost-Crushing Dashboard Shows What Works; Where Others Fall Short deepset’s Industry-First Observability Features Elevate Trust in Generative AI Applications 

deepset Cloud became the first large language model (LLM) platform to provide insights into the precision and fidelity of responses from LLM generative AI through a first-of-its-kind “Groundedness Observability Dashboard.” With this 01/2024 release, the deepset Cloud platform is redefining how generative AI users approach trust, safety, and efficiency in their generative AI applications.

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Understanding Hallucinations for Unparalleled Trust and Safety

The Groundedness Observability Dashboard displays trend data for how well generative AI responses are grounded in the source documents. For the first time, this feature provides a quantifiable score to measure the factuality of an LLM’s output. The results serve as a guide for developers in modifying their RAG setup, fine tuning models, and altering prompts to improve accuracy and reliability of generated responses. Simplified insights into what works enables users to track how well the model can use the provided data to answer queries in a reliable manner. When tracked over time, this allows for comparisons with other widely-available LLM platforms.

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Greater Confidence in Response Quality

deepset Cloud’s Source Reference Prediction generative response annotation is also now generally available. Response Annotation adds academic-style citations to the LLM-generated answer. Those citations reference the respective document on which a statement is based. Users can then review the source material in order to fact-check generated answers or gain a better understanding of the source data in its original context.

The combination of deepset Cloud’s Groundedness Dashboard and Source Reference Prediction gives organizations greater confidence in the quality of the responses in their LLM applications, and provides visibility when an application’s accuracy does not meet requirements.

Using groundedness to optimize retrieval

Groundedness isn’t just a useful metric for measuring the faithfulness of your LLM-generated answers to a knowledge base. It can also be used as a proxy to identify the ideal hyperparameters for your retrieval step. Optimizing the number of documents embedded in the query can reduce your LLM costs by a significant factor. See our accompanying blog post for an example of how this metric was used to reduce LLM costs by 40%, through clever hyperparameter optimization alone.

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A Trust Layer for Generative AI Applications

These new features emphasize deepset’s commitment to building a robust trust layer within generative AI applications. The new features effectively detect hallucinations and provide benchmarking tools, allowing users to make informed decisions about the reliability of their AI models.

[To share your insights with us, please write to sghosh@martechseries.com]

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