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AI
63 posts about AI. Every summary links to the original.
NVIDIA ·
Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron
NVIDIA and CrowdStrike describe an agentic cybersecurity system that links red-agent attack execution with blue-agent detection engineering in a continuous offense-defense testing loop. In an isolated environment modeled on NVIDIA accelerated computing infrastructure, Falcon sensors captured telemetry while Nemotron 3 Ultra orchestrated the defensive workflow and a customized Nemotron 3 Super generated or repaired detections. The harness grounded agents in sensor schemas and attack traces, then applied linting, replay, structured correction, and independent review before deployment. Backtesting raised mean detection of the recorded attack from 16.5% to 41.9% with the optimized open pipeline across independently seeded sessions. In live-fire tests, five of 11 open detections detected an unseen attack, three qualified as gold, and those three covered all eight attacks; the authors call the result a directional case study because it used one scenario family and limited benign traffic.
Michelle HortonBecoming an AI Team
Becoming an AI team requires more than adding assistants to existing workflows: it changes ownership, planning, roles, and execution. At Pinterest, infrastructure teams face the scale of serving billions of Pins, boards, ads, and real-time signals, making AI adoption an operational necessity for reliability, cost efficiency, and developer productivity. AI code-generation and transformation models can turn a widespread legacy-codebase refactor, such as replacing “foo” with “bar,” from a months-long effort into work completed and verified in a week or less. As routine execution is increasingly augmented or automated, engineers, product managers, and designers are expected to spend more time on strategy, problem definition, and user validation, while managers emphasize vision, trade-offs, mentorship, and collective impact. The conclusion is that teams must continuously optimize an AI-driven operating system rather than treat AI as a side project.
Pinterest EngineeringThe new Brickbuilder Partner Network tiers for ISVs and Data Providers are here
The Databricks Partner Network has launched Bronze, Silver, Gold, and Platinum tiers for ISVs and data providers, with placement based on customer impact, technical excellence, strategic alignment, readiness and enablement, and joint go-to-market activity. Technical Excellence is evaluated through the Partner Well-Architected Framework (PWAF), which provides prescriptive, AI-ready guidance for secure, reliable integrations and serves as the standard for validated architecture. For customers, the tiers signal that a partner solution has met a high bar for architecture, security, compliance, and joint customer success, reducing vetting effort. For partners, each tier defines requirements and a roadmap tied to greater visibility, co-selling, and go-to-market support, while Databricks plans to refine the program and expand benefits over the coming months.
Stephen OrbanWhat is an AI Copilot?
An AI copilot is an assistant embedded in a software application that uses workflow context to offer suggestions, generated content, or approved actions while keeping the human in control. Unlike a standalone chatbot, it combines an LLM with contextual grounding, retrieval-augmented generation (RAG), access to enterprise data, and action layers that can call APIs, execute code, trigger workflows, or update records. The explainer surveys code, productivity, data and analytics, customer-facing, and domain-specific copilots, including uses such as SQL generation, dashboard creation, email drafting, and case summarization. Reported benefits include faster routine work, broader data access, more consistent outputs, and less context switching; a cited GitHub study found Copilot users completed a controlled coding task 55% faster. It also stresses hallucinations, privacy, bias, overreliance, integration complexity, cost, and the requirement for human review, governance, and accountability.
Databricks StaffRamp ·
You're Spending Too Much on AI. You're Also Using Too Little.
The post argues that a large AI bill does not show excessive use: companies can overspend on routine work while using too little AI where advanced models could create value. It proposes measuring work in atomic tasks—such as invoices coded or pull requests reviewed—rather than tokens, with cost defined by tasks attempted and value by successful tasks. The operating model uses defaults that pair routine work with the cheapest model meeting quality benchmarks, medium reasoning, and flexible latency, while escalating ambiguous, high-stakes work to frontier models at higher effort. It also recommends attributing spend by provider, product, team, and workflow, finding concentrated costs, benchmarking repeated tasks, repricing after model releases, and centralizing controls in one gateway. The conclusion is that efficiency should be treated as an engineering achievement, so cheaper routine execution funds ambition on rare tasks where extra intelligence can materially change outcomes.
Anand Kuchibotla, Kedar Thakkar, Rahul SengottuveluAnnouncing the new Databricks Startup Program
The Databricks Startup Program has been updated for venture-backed, early-stage startups building with data and AI. Qualifying companies can receive up to $200,000 in credits across Databricks and Neon, along with hands-on technical guidance, partner and community access, and connections to go-to-market teams and founder communities. The program is aimed especially at startups that recently raised institutional funding from pre-seed through Series A, and it is intended to provide an app backend, data, and AI stack from idea through product-market fit. Databricks and Neon are presented as providing a database and access to foundation models on day one, while Databricks supplies analytics, data warehousing, AI systems, and enterprise-grade governance as companies launch. Applications are available through the Databricks Startup Program.
Brad Van Vugt, Arjun RajeswaranDatabricks announces 2026 global partner awards
Databricks announced its 2026 Partner Awards at Data + AI Summit, recognizing more than 65 achievements across its global partner network of over 8,000 organizations. The awards cover consulting and system integrators, independent software vendors, technical champions, learning and enablement, industry categories, and product-focused contributions. Accenture/Avanade received Global Partner of the Year for an eighth consecutive year, citing more than 15,000 trained practitioners, 9,500+ certified resources, and over 1,000 joint engagements. Other cited results include Capgemini’s Unity Catalog migration of 25,000 tables, 10,000 notebooks, and 2 petabytes across 150+ countries, while Kraken Technologies reduced data-processing costs eightfold and cut load times from three days to eight hours using Databricks and Delta Sharing. The announcement frames the winners’ work as supporting data and AI adoption through software, services, integrations, and consulting.
Kori O'Brien, Stephen OrbanSkip the learning curve: rethinking data migration for real outcomes
Data migrations are presented as high-risk, costly initiatives whose technical completion can delay adoption and strategic value, especially during infrequent warehouse transitions. The proposed alternative combines migration, modernization, and value creation in parallel, using experienced specialized partners and AI-enabled automation for code conversion, data-quality validation, and pipeline modernization. Rather than lifting and shifting legacy workloads, teams are urged to simplify architectures, retire unnecessary components, reduce technical debt, and align data with business needs while validating progress continuously. Progressive decommissioning reduces the “double-bubble” period in which old and new systems run together, helping costs fall as workloads move instead of waiting for final completion. The Migrate & Modernize Program connects organizations with partners, and the post reports faster cutovers, reduced migration costs, and complex workloads entering production ahead of schedule among early participants.
Vijay AnalaFrom Wall Street to Data Platforms
Kim Hatton, Databricks’ Global Financial Services Marketing Leader, describes how two decades in regulated financial-services marketing led her toward technology and data-centered strategy. She says financial institutions still need to unlock value while navigating compliance, but their divisions and systems make a unified customer view difficult. Her account points to Unity Catalog’s unified governance and single source of truth for breaking down silos and supporting requirements including GDPR, customer identity, and sensitive workloads. It also describes Lakebase’s separation of compute and storage for faster ML/AI agent experimentation, alongside Genie’s plain-language data analysis, which can reduce work that otherwise takes months and expensive third parties. Hatton connects these tools with faster, more confident marketing and accurate decision-making in regulated workflows, while also describing Databricks’ inclusive, high-energy culture.
Andrea Fernández, Kim HattonWhat is enterprise intelligence?
Enterprise intelligence (EI) is presented as an organization-wide capability combining business intelligence, knowledge management, enterprise search and AI to turn structured and unstructured data into decisions and actions. Unlike traditional BI, which centers on dashboards, reports and structured data, EI connects these capabilities through a shared architecture and governed business context. The described stack includes a lakehouse-based data foundation, batch and streaming pipelines, governance, semantics, analytics, search, machine learning, generative AI and a decision layer. Shared definitions such as “active customer” and “monthly revenue” are intended to keep dashboards, queries and AI agents aligned, while maintained context addresses knowledge that becomes stale as the business changes. The result described is a common trusted source from which people, applications and agents can produce insights and initiate actions.
Databricks StaffTalk to all your data, wherever it lives
Lakehouse Federation addresses the challenge of reasoning across enterprise data spread among AWS Glue, Snowflake, Oracle, BigQuery, Postgres, and legacy formats without first migrating it. By connecting external sources in place and syncing their metadata into Unity Catalog, Databricks applies shared permissions, lineage, and access controls while preserving source data. Federated comments and descriptions give Genie schema context, while Unity Catalog Semantics lets teams define governed metrics such as ROI once for consistent use across Genie, dashboards, and notebooks. The example connects an AWS Glue marketing database, carries its metadata, defines an ROI metric view, and asks Genie which campaigns led ROI last quarter. The post reports an immediate, accurate answer from live Glue data, and describes planned richer semantics, broader federation, and possible performance gains from managed tables.
John SpencerForward Deployed Engineering: Delivering Business Outcomes with AI
Databricks is formalizing its Forward Deployed Engineering (FDE) organization to address customers’ shift from migration and data-pipeline requests toward business outcomes with AI. FDE brings Professional Services together around an engineering-led model that embeds engineers with customers, supports modernization and production AI, and works with partners and Databricks R&D. In the cited examples, teams migrated five-plus petabytes of JPMC Consumer and Community Banking Risk data and more than 500 notebooks in four months, while Fox used Lakebase, AI Search, Databricks Apps, and Model Serving to redesign fan experiences. The organization says its engagements use shared OKRs, rapid prototype-to-production delivery, embedded engineering, outcome-aligned commercial options, and global partner coverage, with Fox reporting that Sports AI users spend approximately twice as long in the app.
Jason MartinWelcoming the first cohort of Databricks student fellows
Databricks announces its inaugural Student Fellows cohort, selected from more than 5,000 applications submitted by students at hundreds of universities and from dozens of countries. The program targets students who contribute on campus, apply data and AI in practice, and intend to pursue careers in the field, with fellows acting as bridges between academic theory and the real-world scale of the Databricks platform. Five students are profiled, with experience spanning large-scale AI systems, ETL pipelines, computer vision, machine learning platforms, robotics, data modeling, and local retrieval-augmented generation architectures. During the coming academic year, the cohort will receive training from Databricks experts and hands-on experience solving complex data challenges, while building a launchpad for future internship opportunities. The program invites applications for its next cohort in Fall 2026 and points readers to Databricks Free Edition.
Elise Hollowed, Joe Nash, Trang LeScaling AI Through Data Fluency
Aer Lingus is redirecting a significant share of its IT and change spending from traditional maintenance toward a Databricks-powered data foundation, addressing legacy systems that trap information in departmental silos. Dave O’Donovan says the airline spent the past 18 months prioritizing platform development, governance, data quality and data literacy rather than chasing each new AI product. Databricks was selected for a unified lakehouse architecture, with data warehousing, Genie’s plain-English querying and real-time operational data intended to broaden access beyond specialist teams. At Aer Lingus’s Operations Control Center, combining sensor and operational inputs gives teams a fuller real-time view for disruption decisions, while commercial teams use live insights to adjust pricing. The transformation also includes a Data Literacy Academy, a 75/25 capacity split between foundational work and innovation, a 20-person Continuous Improvement team, and experiments with agents for business-case development and CFO review.
Aly McGueHow Rivian drives trusted, AI-powered decisions at the speed of thought with Databricks
Rivian is building electric vehicles and services that require fast, trusted decisions across manufacturing, supply chain, finance, service and operational planning, while business users need reliable metrics and insights. Using Databricks AI/BI, Genie, Unity Catalog metric views, Databricks Apps and AI-assisted engineering, the company is consolidating dashboards, semantic definitions, permissions, sensitive data and AI-powered workflows on one governed foundation. Rivian migrated a massive multi-domain dashboard base in less than six months, is standardizing more than 50 metrics, and worked with Databricks as a design partner on roughly 58 product features. The resulting self-service analytics and operational applications cut supply-chain monitoring time by 60 to 70%, reduce inventory investigations from over 30 minutes to under two, predict stock-out risk more than four days ahead, and reduce some ingestion setup time by more than 60%, supporting AI-powered decisions without competing versions of the truth.
Romit Jadhwani, Saritha Suresh, Miranda Luna, Julia PowellAnnouncing the winners of the 2026 Databricks Customer Awards
The 2026 Databricks Customer Awards recognize organizations and leaders using the Databricks Data + AI Platform across eight categories and four regions. The announcement names winners including Applied Materials, Virgin Atlantic, Fonterra Co-operative Group, Telefónica | Vivo, Virtue Foundation, Octopus Energy, Axpo, Atlassian, Wassym Bensaid at Rivian and Volkswagen Group Technologies, and Kenan Colson at Lippert. Examples include Applied Materials’ move from a Hadoop-based data lake to a governed lakehouse, with 1,500-plus analysts, more than 100 production machine-learning models and faster pipeline development, while Fonterra centralized data to improve supply-chain and compliance work. Rivian unified vehicle, factory and enterprise data, consolidated legacy systems into Delta and Unity Catalog, and designed for 500–600 petabytes; Lippert deployed AI tools across customer care, finance and HR. The announcement presents data and AI adoption as a cross-functional operating model.
Sara SteffenAnnouncing the 2026 Databricks Customer Awards Industry winners
Databricks announced its 2026 Customer Awards Industry winners, recognizing 10 organizations across financial services, communications, health and life sciences, manufacturing, retail and CPG, energy and utilities, enterprise technology, public sector, digital-native businesses and cybersecurity. The cited work uses data and AI to address industry-specific needs, including SMBC Group’s governed lakehouse for risk and finance, Hospital for Special Surgery’s full-system ingestion strategy, Lumen’s conversational service-operations workflows and Superhuman’s high-volume model serving. Reported results include HSS ingesting more than 40 source systems and creating over 14,500 production tables, Lumen recording 3 million-plus AI-powered diagnostics and 35% ticket deflection, and Superhuman handling peaks above 200,000 queries per second. Adobe is also recognized for applying software engineering practices to cybersecurity detection workflows, reducing false positives and improving development speed.
Michael GriffithsWhat is Human-in-the-Loop (HITL)?
Human-in-the-loop (HITL) is an AI and machine learning approach that places people in training, supervision, or decision-making to improve accuracy, safety, and ethical alignment. Its feedback loop can include data labeling, output review, escalation, approval, override, and continuous feedback, with confidence thresholds and risk scoring routing only selected decisions to people. The explainer distinguishes HITL, where review occurs before flagged actions, from human-on-the-loop monitoring and human-over-the-loop governance, and separates HITL from RLHF, a training-specific technique. It describes uses in medical imaging, moderation, autonomous vehicles, financial services, and AI agents handling consequential actions. Databricks Agent Bricks is presented as supporting governed traces and Agent Learning from Human Feedback, including a case where 32 feedback items improved instruction-following from roughly 12% to 80%.
Databricks StaffWhat is Explainable AI (XAI)?
Explainable AI (XAI) comprises techniques that help people understand how AI systems produce specific outputs, particularly when machine-learning and deep-learning models operate as black boxes. It distinguishes intrinsically interpretable models, such as decision trees and linear or logistic regression, from post-hoc methods including SHAP, LIME, counterfactuals, saliency maps and Grad-CAM. A typical workflow selects a model and prediction, applies a method suited to the model and audience, reviews outputs such as feature scores or heatmaps, and uses them to assess accuracy, fairness, reliability and compliance. The article stresses that post-hoc explanations are approximations rather than definitive proof, so teams should validate them with domain expertise and, where appropriate, combine methods; MLflow and Unity Catalog can preserve explanation artifacts, lineage and auditability.
Databricks StaffData + AI Summit 2026: Insider’s Guide for Financial Services Leaders
Data + AI Summit 2026 features a dedicated financial services program for leaders evaluating AI transformation across banking, payments, insurance, and professional services. It lists sessions on proprietary data for underwriting, responsible AI in banking and payments, and AI delivery in professional services featuring First American, American Modern Insurance Group, Vantage Bank Texas, Santander, FIS, Acxiom, EXL, Bain, and EY. The Financial Services Forum includes executive firesides with leaders from Morgan Stanley, JPMorganChase, Mastercard, and RBC Capital Markets. A financial services lounge at the Moscone Expo offers demos, Databricks experts, and Agentic Banker and Virtual CFO use cases. Training courses on AI Agents, Lakebase, and apps plus hands-on labs and certification sessions are presented as a route from strategy to execution with executives and technical teams splitting focus.
Kim Hatton
