News from the AI & ML world
DeeperML - #databricks
|
Mike Wheatley@SiliconANGLE
//
Databricks Inc. has unveiled Databricks One, an AI-powered business intelligence tool designed to democratize data and AI accessibility for all business workers, regardless of their technical skills. This new platform aims to simplify the way enterprises interact with data and AI, addressing the challenges of complexity, rising costs, and vendor lock-in that often hinder the practical application of data insights across organizations. Databricks One introduces a simplified user interface, making the platform's capabilities accessible to individuals who may not possess coding skills in Python or Structured Query Language.
Databricks One offers a code-free, business-oriented layer built on top of the Databricks Data Intelligence Platform, bringing together interactive dashboards, conversational AI, and low-code applications in a user-friendly environment tailored for non-technical users. A key feature of Databricks One is the integration of a new AI/BI Genie assistant, powered by large language models (LLMs). Genie enables business users to ask questions in plain language and receive responses grounded in enterprise data, facilitating detailed data analysis without the need for coding expertise. The platform utilizes generative AI models, similar to interfaces like ChatGPT, allowing users to describe the type of data analysis they want to perform. The LLM then handles the necessary technical tasks, such as deploying AI agents into data pipelines and databases to perform specific and detailed analysis. Once the analysis is complete, Databricks One presents the results through visualizations within its interface, enabling users to further explore the data with the AI/BI Genie. Databricks One is currently available in private preview, with a private beta planned for later in the summer. Recommended read:
References :
Kuldeep Jha@Verdict
//
Databricks has unveiled Agent Bricks, a new tool designed to streamline the development and deployment of enterprise AI agents. Built on Databricks' Mosaic AI platform, Agent Bricks automates the optimization and evaluation of these agents, addressing the common challenges that prevent many AI projects from reaching production. The tool utilizes large language models (LLMs) as "judges" to assess the reliability of task-specific agents, eliminating manual processes that are often slow, inconsistent, and difficult to scale. Jonathan Frankle, chief AI scientist of Databricks Inc., described Agent Bricks as a generalization of the best practices and techniques observed across various verticals, reflecting how Databricks believes agents should be built.
Agent Bricks originated from the need of Databricks' customers to effectively evaluate their AI agents. Ensuring reliability involves defining clear criteria and practices for comparing agent performance. According to Frankle, AI's inherent unpredictability makes LLM judges crucial for determining when an agent is functioning correctly. This requires ensuring that the LLM judge understands the intended purpose and measurement criteria, essentially aligning the LLM's judgment with that of a human judge. The goal is to create a scaled reinforcement learning system where judges can train an agent to behave as developers intend, reducing the reliance on manually labeled data. Databricks' new features aim to simplify AI development by using AI to build agents and the pipelines that feed them. Fueled by user feedback, these features include a framework for automating agent building and a no-code interface for creating pipelines for applications. Kevin Petrie, an analyst at BARC U.S., noted that these announcements help Databricks users apply AI and GenAI applications to their proprietary data sets, thereby gaining a competitive advantage. Agent Bricks is currently in beta testing and helps users avoid the trap of "vibe coding" by forcing rigorous testing and evaluation until the model is extremely reliable. Recommended read:
References :
Kuldeep Jha@Verdict
//
Databricks has unveiled Agent Bricks, a no-code AI agent builder designed to streamline the development and deployment of enterprise AI agents. Built on Databricks’ Mosaic AI platform, Agent Bricks aims to address the challenge of AI agents failing to reach production due to slow, inconsistent, and difficult-to-scale manual evaluation processes. The platform allows users to request task-specific agents and then automatically generates a series of large language model (LLM) "judges" to assess the agent's reliability. This automation is intended to optimize and evaluate enterprise AI agents, reducing reliance on manual vibe tracking and improving confidence in production-ready deployments.
Agent Bricks incorporates research-backed innovations, including Test-time Adaptive Optimization (TAO), which enables AI tuning without labeled data. Additionally, the platform generates domain-specific synthetic data, creates task-aware benchmarks, and optimizes the balance between quality and cost without manual intervention. Jonathan Frankle, Chief AI Scientist of Databricks Inc., emphasized that Agent Bricks embodies the best engineering practices, styles, and techniques observed in successful agent development, reflecting Databricks' philosophical approach to building agents that are reliable and effective. The development of Agent Bricks was driven by customer needs to evaluate their agents effectively. Frankle explained that AI's unpredictable nature necessitates LLM judges to evaluate agent performance against defined criteria and practices. Databricks has essentially created scaled reinforcement learning, where judges can train an agent to behave as desired by developers, reducing the reliance on labeled data. Hanlin Tang, Databricks’ Chief Technology Officer of Neural Networks, noted that Agent Bricks aims to give users the confidence to take their AI agents into production. Recommended read:
References :
@siliconangle.com
//
Databricks is accelerating AI capabilities with a focus on unified data and security. The Data + AI Summit, a key event for the company, highlights how they are unifying data engineering, analytics, and machine learning on a single platform. This unified approach aims to streamline the path from raw data to actionable insights, facilitating efficient model deployment and robust governance. The company emphasizes that artificial intelligence is only as powerful as the data behind it, and unified data strategies are crucial for enterprises looking to leverage AI effectively across various departments and decision layers.
Databricks is also addressing critical AI security concerns, particularly inference vulnerabilities, through strategic partnerships. Their collaboration with Noma Security is aimed at closing the inference vulnerability gap, offering real-time threat analytics, advanced inference-layer protections, and proactive AI red teaming directly into enterprise workflows. This partnership, backed by a $32 million Series A round with support from Databricks Ventures, focuses on securing AI inference with continuous monitoring and precise runtime controls. The goal is to enable organizations to confidently scale secure enterprise AI deployments. The Data + AI Summit will delve into how unified data architectures and lakehouse platforms are accelerating enterprise adoption of generative and agentic AI. The event will explore the latest use cases and product announcements tied to Databricks' enterprise AI strategy, including how their recent acquisitions will be integrated into their platform. Discussions will also cover the role of unified data platforms in enabling governance, scale, and productivity, as well as addressing the challenge of evolving Unity Catalog into a true business control plane and bridging the gap between flexible agent development and enterprise execution with Mosaic AI. Recommended read:
References :
|
BenchmarksBlogsResearch Tools |