Dataiku Launches LLM Guard Services to Provide a Unified Framework for GenAI Success

In today’s dynamic technological landscape, organizations are racing to leverage the power of GenAI to gain a competitive edge, underscoring the critical need for efficient integration of these solutions into their operations.

However, as organizations strive to implement these transformative technologies, they often encounter challenges such as managing escalating costs, maintaining quality standards, and mitigating operational risks.

Dataiku, a leader in enterprise AI and machine learning, provides a solution to address these challenges and mitigate common risks associated with building, deploying, and managing GenAI within organizations.

The New York-based company has introduced its LLM Guard Services suite. This new offering is designed to streamline the deployment of GenAI at scale, enabling businesses to transition from proof-of-concept projects to full-scale production while ensuring performance and compliance.

The LLM Data Guard services combine three core solutions – Safe Guard, Cost Guard, and the new addition Quality Guard. The introduction of LLM Data Guard services addresses the lack of specific applications for managing LLMs.

The Safe Guard solution assesses requests and responses for sensitive data, implementing customizable features to prevent data misuse and leaks, thereby securing LLM applications.

Meanwhile, Cost Guard serves as a dedicated monitoring tool that enables organizations to effectively track and manage their LLM usage, helping them anticipate expenses and ensure alignment with budgetary expectations.

Quality Guard, the latest addition to the suite, automates and standardizes the evaluations of LLM response, ensuring high-quality outputs based on objectivity and scalability.

In the past, organizations implementing GenAI had to rely on custom code-based methods or specialized solutions that addressed only specific issues to evaluate the performance of LLMs, making the process complex and fragmented.

Now, with Quality Guard, users can streamline the evaluation of GenAI models by automating the calculation of key metrics, including answer relevance and correctness. It leverages advanced techniques such as LLM-as-a-judge, BERT, ROUGE, and BLEU to assess model quality, providing a more accurate and efficient approach to selecting the best models for their needs. This integration allows enterprises to sustain GenAI reliability over time with greater predictability, ultimately democratizing GenAI applications for all stakeholders involved.

The three components are integrated within the Dataiku LLM Mesh—an advanced framework for managing and deploying enterprise-grade GenAI applications. LLM Guard Services enhances this framework by providing a scalable no-code solution that promotes transparency, fosters inclusive collaboration, and builds trust in GenAI projects among teams across organizations.

Modern enterprises aim to streamline their data management by minimizing the number of tools they utilize, which helps alleviate the challenges of scaling projects that involve siloed systems. However, a recent Dataiku survey reveals that 88% of organizations lack specific applications or processes for managing LLMs.

With the new LLM Data Guard services, data teams can operate on a unified platform, facilitating the creation, testing, and deployment of their GenAI applications. This streamlined approach not only enhances efficiency in their workflows but also allows them to better meet the expectations of their stakeholders.

“As the AI hype cycle follows its course, the excitement of two years ago has given way to frustration bordering on disillusionment today. However, the issue is not the abilities of GenAI, but its reliability,” said Florian Douetteau, Dataiku CEO.

“Ensuring that GenAI applications deliver consistent performance in terms of cost, quality, and safety is essential for the technology to deliver its full potential in the enterprise. As part of the Dataiku Universal AI platform, LLM Guard Services is effective in managing GenAI rollouts end-to-end from a centralized place that helps avoid costly setbacks and the proliferation of unsanctioned ‘shadow AI’ – which are as important to the C-suite as they are for IT and data teams.”

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