Knowledge catalogue
AI agents struggle to effectively process and extract information from complex enterprise documents due to limitations in context windows, reasoning capabilities, and handling of unstructured data for
Databricks' AI Gateway provides a centralized governance and control layer designed to manage agentic AI systems, enabling organizations to enforce policies, monitor usage, and maintain oversight acro
**Managing AI Coding Costs at Scale** This article addresses the economic challenges of deploying AI coding tools across large teams or organizations. It explores strategies for tracking resource usag
pgvector is an open-source PostgreSQL extension that enables efficient storage and similarity search of vector embeddings within a relational database. It allows developers to build vector search capa
This article examines the infrastructure approaches and strategies that leading AI companies use to support their AI development and deployment at scale. It likely covers key components such as data p
Decision Execution Platforms represent an evolution beyond traditional dashboards by enabling organizations to not only visualize data insights but also automatically trigger and execute business deci
This appears to be a casual social media post from Swyx linking to a Latent Space article about Databricks, mentioning Neon Database and other developers. The post likely discusses Databricks' technol
Serverless PostgreSQL is a managed database service that abstracts away infrastructure management, allowing users to run PostgreSQL databases without provisioning or managing servers. This approach en
A serverless database for AI applications should prioritize scalability, low latency, and seamless integration with machine learning workflows, eliminating the need for infrastructure management. Key
Vector search is a retrieval method that converts unstructured data (text, images, audio) into numerical vector representations and finds similar items by comparing vectors in high-dimensional space,
Retrieval Augmented Generation (RAG) is a technique that combines information retrieval with generative AI to enhance model outputs by fetching relevant external documents or data before generating re
This article explores the applications and impact of artificial intelligence on national security and defense operations, examining how AI technologies are transforming military capabilities, threat d
This guide provides best practices and architectural principles for designing effective data warehouses, covering topics such as schema design, performance optimization, and scalability considerations
This article discusses how healthcare organizations can move beyond basic X12 EDI parsing to implement more comprehensive revenue cycle management workflows using data analytics platforms. It likely a
This article argues that AI projects fail to scale when organizations treat them as one-off innovations rather than integrating them into standard business operations and processes. According to Datab
This resource discusses how healthcare organizations can build learning health systems to succeed under CMS TEAM (Transforming Episode Accountability Models) and advance value-based care (VBC) initiat
This article discusses how organizations can leverage observability data—including metrics, logs, and traces—to proactively identify and prevent incidents before they impact systems and users. It like
This guide provides Chief Financial Officers with strategies for financial management and performance optimization in value-based care models, which shift healthcare reimbursement from volume-based to
Unity AI Gateway introduces new enterprise features including service policies, guardrails, observability tools, and cost controls designed to manage and monitor AI agents and Model Context Protocols
Chief Marketing Officers are increasingly prioritizing AI security infrastructure due to growing risks around data protection, model integrity, and regulatory compliance in AI systems. Organizations m
AI agents are automating traditional manual processes in electric grid operations, improving efficiency and response times for grid management tasks. These autonomous systems leverage machine learning
Clinical operations intelligence in healthcare can be effectively managed and analyzed using a lakehouse architecture, which combines the benefits of data lakes and data warehouses. This approach enab
Data quality is a foundational component of effective AI strategy, as high-quality training and operational data directly impacts model performance, reliability, and business outcomes. The article lik
Claroty developed an AI-powered library that serves as a translation tool for industrial control system (CPS) protocols and communications, helping security teams understand and analyze diverse indust
This article explores how healthcare organizations can uncover insights about operating room efficiency and utilization rates by analyzing scheduling data, likely demonstrating techniques for identify
This article discusses how AI technologies can help Human Resources departments address growing capacity challenges and workload demands. It likely explores how AI tools can automate routine HR tasks,
Growth analytics represents the next evolution beyond growth hacking, shifting focus from rapid experimentation and acquisition tactics to data-driven analysis of sustainable growth patterns and custo
Telecom companies often fail to act on churn predictions within the critical timeframe needed to retain customers, as the lag between model prediction and business intervention allows customers to com
Mean time to detect (MTTD) is fundamentally constrained by data access capabilities, as organizations cannot identify security incidents or anomalies faster than they can query and analyze their data.
First-party audience data has become central to modern advertising sales relationships, replacing traditional third-party data dependencies as privacy regulations and cookie deprecation reshape the di
This article argues for democratizing public health data analytics by making intelligence and insights accessible to non-technical public health professionals without requiring specialized data scienc
Enterprise AI implementations often fail due to insufficient focus on talent transformation and workforce upskilling, despite heavy investment in technology infrastructure. Organizations need to syste
Digital native companies often face significant challenges in scaling AI systems beyond initial proof-of-concept stages, despite their technical advantages and cloud infrastructure. The article examin
Data quality is a foundational prerequisite for successful AI applications, often more important than model complexity or sophistication. The article emphasizes that clean, well-organized data directl
The Federal Data Paradox examines how U.S. federal agencies accumulate vast amounts of data but face significant barriers to accessing and utilizing it effectively. The article likely discusses challe
Model risk governance and risk intelligence are distinct but complementary practices in machine learning operations. Model risk governance focuses on establishing policies, controls, and compliance fr
This article likely discusses predictive maintenance and failure detection in industrial turbines, examining how data analytics and machine learning can identify warning signs before catastrophic fail
Overall Equipment Effectiveness (OEE) dashboards often present misleading metrics due to data quality issues, incomplete visibility into production factors, or misalignment between how OEE is calculat
Alert fatigue occurs when security teams receive excessive alerts, causing them to miss critical threats due to desensitization and reduced response effectiveness. This represents a significant busine
This article discusses how clinical trials often extend beyond necessary timelines, which negatively impacts patient access to potentially beneficial treatments and increases development costs. The pi
Network quality issues have direct business and revenue implications beyond just technical performance metrics, requiring organizations to align network reliability with business outcomes and customer
This article emphasizes that healthcare organizations must move beyond simply predicting hospital readmissions to taking timely preventive actions based on those predictions. It likely discusses how m
This article argues that rapid software deployment and iteration do not necessarily translate to faster organizational learning or improved decision-making in data and AI projects. It likely explores
Tomorrow we go live with @Replit CTO @lhchavez to nerd out on how they approach security at the platform and individual project level. 🔐 If you're a professional vibe coder focused on building softwar
This guide covers the fundamentals and best practices for developing AI-powered applications, likely including key stages from ideation through deployment, technology stack considerations, and integra
This article explores the implementation of Internet of Things technology in manufacturing environments, covering strategic approaches, key hardware and software components, practical applications acr
Financial services institutions (FSIs) traditionally rely on fragmented, siloed systems — such as portfolio platforms, reconciliation software, and ERP tools — that prevent client-facing, operation...
**Agentic Workflows** enable automated systems to execute complex tasks by orchestrating multiple agents that interact with each other and external services. They delegate subtasks, monitor progress,
Agentic AI empowers telecom finance teams to preserve margins by rapidly detecting and preventing revenue leakage across billing, provisioning, and cost‑allocation processes. The technology automates
This guide explains how AI customer service operates, focusing on the use of AI agents and sentiment analysis to interpret and respond to customer interactions. It provides practical instructions for
Genie Code surpasses three leading coding agents on more than 400 real‑world coding tasks, achieving the highest accuracy among the compared solutions. It also reduces cost by roughly 50 % per task an
Retail Finance teams are increasingly deploying agentic AI to safeguard omni‑channel margins. These systems automatically monitor sales performance, forecast demand, and adjust pricing or inventory co
The article discusses the disparity between healthcare organizations' capabilities in leveraging data and AI for ambulatory care versus inpatient settings, highlighting how ambulatory providers often
This guide provides criteria and best practices for assessing enterprise analytics platforms, likely covering key evaluation factors such as scalability, performance, integration capabilities, ease of
Imperial College London is leveraging a modern data platform to accelerate dementia research by enabling faster analysis of large-scale datasets and improved collaboration among researchers. The platf
DataOps is a set of practices and tools that apply DevOps principles to data engineering, focusing on automating data pipelines, improving data quality, and enabling faster iteration cycles. The strat
Predictive quality extends beyond traditional defect detection by using data analytics and machine learning to anticipate quality issues before they occur rather than simply identifying existing defec
AI-native cybersecurity refers to security systems designed with artificial intelligence as a fundamental architectural component rather than as an added afterthought, enabling more effective threat d
This article discusses the practical implementation of AI systems in government agencies to detect and prevent fraudulent activities, likely covering deployment challenges, best practices, and real-wo
This guide addresses updated regulatory requirements for model risk management in banking institutions, likely covering the 2026 revisions to interagency guidance issued by banking regulators. It prov