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The platforms, pipelines and real-time architectures that production AI runs on.
Analysis · Data & AI Infrastructure
Part of our midterm election coverage: Democrats won control of Congress, according to projections, along with several data center states. Here is what newly empowered lawmakers have proposed, how industry is responding and where a slowdown could happen.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
Nvidia says its containerized DSX POD can cut AI factory deployment from six months to 14 days. The claim is real but narrow, and the week's news shows the bigger bottlenecks sit in power, labor and the chip supply chain.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
The two companies at the base of the AI chip supply chain reported strong third quarters and warned that capacity will stay tight for years, a signal that enterprise AI compute will remain scarce and costly.
By The AI Enterprise Magazine Team ·
What does a single source of truth mean when AI agents consume and create data? Leaders from SAP, Turgon, Skyflow and AtScale point to semantic layers, permissions that travel with data, agent-generated data and evals against a company's own KPIs.
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
At the Open Compute Project Global Summit, Nvidia and AMD introduced open rack designs built around CPUs for AI agents. The shift changes what AI-ready infrastructure needs, and how much choice enterprise buyers will really get.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
A 20-year contract funds $4.3 billion in upgrades at 11 Constellation reactors, part of a 3.6 GW package that shows power, not just chips, now shapes how much AI capacity enterprises can buy.
By The AI Enterprise Magazine Team ·
Claude Haiku 5.5, OpenAI's Decisions API and Google's Nano Banana 2.1 all lowered the cost of AI this week. But tokenizer changes, agent volume and uncertain long-term pricing mean enterprises should measure cost per task, not per token.
By The AI Enterprise Magazine Team ·
AI agents can only act as fast as their data allows, says IBM's Ann Leach. She makes the case for the streamhouse, a real-time layer that complements the lakehouse, and points to Ferrari, which used live telemetry and AI to double daily app users.
By The AI Enterprise Magazine Team ·
AI stalls on old data problems that need a new fix, argues Actian CTO Emma McGrattan: treating data as a product and governing it with data contracts. She cites survey results linking company-wide data products to AI in production and offers a one-project way to start.
By The AI Enterprise Magazine Team ·
AI has no control plane yet, leaving agents loose in brownfield data estates, argues NexusOne CEO Anu Jain. He lays out three questions every leader should answer, the case for one identity across all systems, and why companies should own their ontologies.
By The AI Enterprise Magazine Team ·
When an AI agent gets a refund policy wrong, the model often never saw the policy. Redis Senior Solution Architect Jessica Emmons breaks down context engineering, the four ways context fails, why basic RAG isn't enough and how context engines, agent memory and semantic caching help.
By The AI Enterprise Magazine Team ·
Good AI ideas usually stall waiting on other teams, separate systems and security reviews, says Google Cloud's Shan Kulandaivel. Forecasting, prediction, document processing, graph and vector search inside BigQuery let SQL users take those ideas to production, he argues.
By The AI Enterprise Magazine Team ·
Matillion's Maia agent builds, tests and documents data pipelines from plain-language requests. Matillion's Arawan Gajajiva argues that data engineers' work is shifting from writing code to governing, planning and evaluating what AI builds.
By The AI Enterprise Magazine Team ·
Harvey's gross margin reportedly swung from 50% to negative 50% as agent usage soared, until it moved work to a model built on open weights. Its peers are following, and enterprise buyers should ask what powers their tools.
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
California now requires data centers to pay for the grid upgrades they trigger and to disclose energy and water use. Supporters call it ratepayer protection, industry warns of lost competitiveness, and enterprise AI buyers should expect the costs to surface.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
A bipartisan 417-3 vote asks state regulators to make large data centers cover the grid upgrades they require, a signal that the power costs of AI capacity are moving toward the companies that build it, and their customers.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
Oracle says AI cloud demand still outruns supply after infrastructure revenue more than doubled and its backlog reached $664 billion. Negative free cash flow and new equity sales kept investors cautious.
By The AI Enterprise Magazine Team ·
Feature · Data & AI Infrastructure
Washington is underwriting a nuclear restart tied to Google while Massachusetts makes big data centers bring their own clean power and win community approval. Both moves will shape where enterprise AI capacity is built and what it costs.
By The AI Enterprise Magazine Team ·
Snowflake's Tyler Richards used Cortex Code to build a dashboard and Slack alerts from written specs, while Precisely's Carmen Adame had an AI assistant assemble a property risk profile through an MCP server.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
Broadcom's AI semiconductor revenue rose 221% to $16.7 billion as Google, Anthropic, OpenAI and Meta ramp custom chips. For enterprises, the shift changes who controls AI unit costs and where capacity bottlenecks sit.
By The AI Enterprise Magazine Team ·
Enterprise AI has moved from an era of speed to an era of control, says Fulcrum Digital founder Rajesh Sinha. He lays out a seven-layer sovereign AI architecture, why many projects fail at the inference layer and agent use cases in insurance, finance and manufacturing.
By The AI Enterprise Magazine Team ·
The best-funded AI programs will pay for themselves by reclaiming wasted data and AI spend, argues Revefi's Girish Bhat. Verisk Global Chief Data Officer Louis Dimodugno describes how Snowflake savings covered the cost of the tooling, and why token prices may soon rise.
By The AI Enterprise Magazine Team ·
Fragmented systems, stale data and reactive governance hold back most AI projects, says IBM's Tim Richer. He identifies the use cases scaling fastest, how IBM runs agents on its own HR, IT and procurement systems, and client results from Ferrari and EY.
By The AI Enterprise Magazine Team ·
The fastest path to production AI is running it where sensitive data already lives, argues Everpure VP of AI Kaycee Lai. He lays out what AI-ready data requires, why unstructured data is the biggest blocker and who he thinks will win the AI race.
By The AI Enterprise Magazine Team ·
Data lakes and lakehouses were built for human users, not agents that run around the clock and scale to thousands, says Teradata's Ben Sanders. He explains why pilots break in production, how missing context drives up cost and latency, and the six components of enterprise context.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
Nvidia beat forecasts with $96.2 billion in revenue and guided to $108 billion next quarter. For enterprise buyers, the results point to tight GPU supply, rising component costs and a Rubin transition to plan around.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
Pennsylvania's governor made state permits conditional on developers bringing their own power and winning local approval, days after PJM asked regulators to curtail new data centers first in shortages. Grid access is becoming something AI capacity must earn.
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
Databricks raised $5 billion at a $190 billion valuation and reported a run-rate above $7 billion. The numbers show enterprises moving AI budgets into the data layer, along with valuation and lock-in risks leaders should weigh.
By The AI Enterprise Magazine Team ·
Four vendors are taking different routes to enterprise AI: Mistral AI with document OCR and voice models, SAS with detection of AI-generated forgeries, Fivetran with one dataset served to seven query engines and Precisely with location data for Claude via MCP.
By The AI Enterprise Magazine Team ·
Data built for human analysts isn't ready for autonomous agents, say experts from Snowflake and Precisely. They call for rich metadata, shared data quality rules, lineage and auditability as the foundation for agentic AI.
By The AI Enterprise Magazine Team ·
Leaders from SAP, Skyflow and Fivetran lay out the architecture behind AI that scales: isolation boundaries, model-agnostic interfaces, agents that survive tool failures, and observability that tracks why agents touch data.
By The AI Enterprise Magazine Team ·
Supermetrics has Claude analyzing Facebook ads, launching campaigns and publishing live dashboards, while Precisely uses an MCP server to let Claude find and fix SAP material data. Both move AI from reading enterprise data to acting on it.
By The AI Enterprise Magazine Team ·
AI agents silently degrade after launch, say Snowflake's Sam Mittal and Monte Carlo's Ethan Post. They outline Cortex Agents' trust features, a four-layer framework for diagnosing bad answers, how stale data skews results and six early warning signals to track.
By The AI Enterprise Magazine Team ·
Leaders from Collate, Graphwise, Precisely and AtScale disagree on whether metadata is enough for the AI context layer, agree that RAG and memory work together, and predict a fight over who controls enterprise semantics.
By The AI Enterprise Magazine Team ·
AI leaders from Snowflake, Revefi and The Coca-Cola Company say most AI pilots stall for organizational reasons, not technical ones. They shared a three-part test for deciding what to scale, the infrastructure that holds up in production, and a 70-20-10 rule for where to spend effort.
By The AI Enterprise Magazine Team ·
Teradata built a LangChain agent that checks loyalty discount eligibility using a vector store and a governed SQL tool. Coder shares what its experiments with agent harnesses, sub-agents and context management did and didn't improve.
By The AI Enterprise Magazine Team ·
Prompt engineering won't fix AI accuracy, say leaders from AtScale and Rhino Federated Computing. They outline what production AI infrastructure needs and how to control GPU and inference costs as agents arrive.
By The AI Enterprise Magazine Team ·
Data warehouses built for weekly reports leave AI agents with stale context, say Striim and Google Cloud experts. Change data capture, event stream processing and MCP can feed agents live operational data on Google Cloud instead.
By The AI Enterprise Magazine Team ·
Striim's Jake Bengtson and Google Cloud's Naveen Punjabi say AI stalls in production when data is stale, pulled straight from operational systems or stripped of business meaning. They outlined four signs your data can support AI agents at scale.
By The AI Enterprise Magazine Team ·
Mistral AI fine-tuned a model, Google Cloud ran agent-driven churn analysis, Aerospike powered real-time nudges, Precisely added location context, Striim masked data in flight and PromptQL flagged its own assumptions: six takes on practical enterprise AI.
By The AI Enterprise Magazine Team ·
Vibe-coded prototypes are easy; production AI is not. Leaders from DataHub, Precisely, Snowflake and Fivetran explained what separates AI-enabled from enterprise-grade, where companies underinvest, how to govern agents and how much of a prototype survives the move to production.
By The AI Enterprise Magazine Team ·
Agentic data management pays off most for the analysts, data scientists and leaders stuck in the data team's queue, Matillion's field engineering team argues. Its Maia agent offers self-service pipeline building, permission prompts, context files that enforce standards and AI-driven troubleshooting.
By The AI Enterprise Magazine Team ·
AI fails on weak data foundations rather than algorithms, say field CTOs from Fivetran and Snowflake and an Accenture managing director. They define AI-ready data and describe how quality, access, governance and flexible infrastructure separate leading teams.
By The AI Enterprise Magazine Team ·
Supermetrics puts governed marketing data in Claude through MCP, Striim streams real-time operational data with PII masking, and Lumi AI builds an AI data analyst on a curated knowledge base: three approaches to giving agents data they can trust.
By The AI Enterprise Magazine Team ·
Data leaders from AtScale, Snowflake, Denodo, Fivetran and Revefi shared how executives can separate AI hype from real opportunities. They offered criteria for picking use cases, advice for CIOs under board pressure, a framework for build-versus-buy decisions and examples of projects that delivered measurable returns.
By The AI Enterprise Magazine Team ·
Clients love charts but rarely know which ones they want. Broadband Insights CEO Patrick Hutto says his team uses vibe analytics with Plotly's AI Studio to explore data with clients in real time, then moves the best views into production.
By The AI Enterprise Magazine Team ·
Banks and payment companies get about 50 milliseconds to approve a card swipe. Aerospike's Sesh Seshadri says database speed decides how much machine learning, graph analytics and multi-stage inference fit in that window.
By The AI Enterprise Magazine Team ·
Data and AI leaders from The Home Depot, Equifax and Mars Wrigley tie every AI use case to a business OKR, trade off speed, accuracy and cost, and argue that AI belongs in the middle of the process, not first.
By The AI Enterprise Magazine Team ·
Infrastructure cost decides whether real-time AI pays off, argues Aerospike founder and CTO Srini Srinivasan. He explains the company's hybrid memory architecture, how faster lookups improve fraud and recommendation decisions, and why agents will multiply database load.
By The AI Enterprise Magazine Team ·
IBM's Ryan Zombo lays out five questions enterprises must answer to move agentic AI into production, from avoiding agent sprawl to avoiding lock-in. Governance works like brakes on a race car, he argues, citing productivity results from IBM's internal client zero program.
By The AI Enterprise Magazine Team ·
Google Cloud's data science agent can plan and build a demand forecast in a BigQuery notebook, even fixing its own error along the way. Google Cloud's Suda Srinivasan calls agents an exoskeleton for data scientists, not a replacement.
By The AI Enterprise Magazine Team ·
No one is closer to enterprise data than the integration platform, argues SnapLogic Senior Solutions Engineer Dylan Vetter. He explains how IT teams can share governed integration assets across business units and build custom MCP servers that connect LLMs to company data.
By The AI Enterprise Magazine Team ·
AI pilots often fail in production because they run on siloed slices of untrusted data. Syncari Principal Solutions Engineer Jack Smith argues that more integration tools and data lakes won't fix that, and makes the case for a centralized, agentic master data layer.
By The AI Enterprise Magazine Team ·
Grid Dynamics built an internal agent mesh for AI agent collaboration. Search and AI Architect Stas Stolpovskiy explains what a central team must control, why platforms need a few strict rules and lots of freedom, and why he thinks MCP falls short for agent-to-agent work.
By The AI Enterprise Magazine Team ·
In a load test, Aerospike Solution Architect Stan Christ took two of eight database nodes offline and the cluster rebalanced without help from IT. Aerospike's graph-based fraud detection also blocked suspicious transfers by tracing relationships many hops away.
By The AI Enterprise Magazine Team ·
Snowflake, Denodo, Zenlytic, PromptQL, Fivetran, Google Cloud and Precisely are attacking the same problem from different angles: conversational agents, logical data layers, verified metrics, confidence signaling, fresh pipelines, the Agent2Agent protocol and location data through MCP.
By The AI Enterprise Magazine Team ·
Recommendations, search, RAG and AI agents share more than they seem. Aerospike Chief Evangelist Sesh Seshadri says multi-step inference, feature stores and short-lived agent data sets shape real-time AI infrastructure, drawing on examples from Myntra, PayPal and Barclays.
By The AI Enterprise Magazine Team ·
Searching video and images means keeping context that flat vectors lose. Vespa.ai's Bonnie Chase and Zohar Nissare-Houssen explain how tensors, co-located compute and multi-phase ranking let a hybrid query find a single scene inside a cartoon.
By The AI Enterprise Magazine Team ·
Data leaders from Fivetran, General Mills and Webster Bank make the case for lakehouses in enterprise AI, from open table formats and fine-grained governance to cost control and migrating from a warehouse without losing momentum.
By The AI Enterprise Magazine Team ·
Seven vendors are putting generative AI to work on enterprise data, from Snowflake Cortex Agents and Gemini 2.5 on Vertex AI to self-service AI analytics, contextual location data, data pipelines and agents that write and debug code.
By The AI Enterprise Magazine Team ·
Three HPE and NVIDIA technologists lay out the AI factory model for enterprise AI infrastructure: packaged, turnkey and custom deployments, why cooling now shapes data center design, and the strategy and skills gaps that leave GPUs sitting idle.
By The AI Enterprise Magazine Team ·
AI can narrow the search space for physics-based simulation, says HPE Federal AI Chief Technologist Steve Heibein, citing COVID research, weather and NASA's AI-designed parts. Rising GPU power, he adds, is pushing data centers to liquid cooling.
By The AI Enterprise Magazine Team ·
Nations want to produce AI, not just consume it. Leaders from NVIDIA and HPE define sovereign AI across data, operational and technology sovereignty, and explain why sensitive-data rules and air-gapped environments keep many AI projects from reaching production.
By The AI Enterprise Magazine Team ·
Enterprise GenAI faces a higher bar than consumer AI tools. Leaders from AtScale, Nexla, Informatica, Vespa and Skyflow weigh in on data minimization, semantic layers, single-number evaluation sets, architecture for scale and whether MCP will matter.
By The AI Enterprise Magazine Team ·
Every data leader may have an AI mandate, but many say their data isn't ready. Monte Carlo's Shane Murray and Michael Segner identify the four places AI pipelines break, how AI agents can write monitors and trace root causes, and how to set quality levels by use case.
By The AI Enterprise Magazine Team ·
Snowflake's Cortex Agents work across structured and unstructured data, TiDB builds a knowledge graph for GraphRAG inside one database, and Zenlytic answers an open-ended marketing question with governed metrics: three approaches to AI agents on enterprise data.
By The AI Enterprise Magazine Team ·
Small internet providers lack the data teams of big carriers. Broadband Insights CEO Patrick Hutto details the Python stack behind his firm's production data apps, from Redis caching to a text-to-SQL chatbot, and how one daily report cut install times from months to days.
By The AI Enterprise Magazine Team ·
The biggest costs of production machine learning are often idle people, duplicated pipelines and models built before value is proven. Leaders from Tecton, Mosaic Health and JPMorgan Chase's Morgan Health explain how to scale ML while keeping costs in check.
By The AI Enterprise Magazine Team ·
Real-time AI pays off only when fresh data changes the prediction. Leaders from Tecton, Sam's Club and AT&T explain how to combine precomputed and streaming features, catch silent pipeline failures and cut the cost of real-time machine learning.
By The AI Enterprise Magazine Team ·
Text extraction throws away the charts and tables in visually rich documents. Vespa CEO Jon Bratseth and RavenPack's Aakarsh Ramchandani explain how ColPali retrieves PDF pages by what they look like, where it still falls short and how to scale it.
By The AI Enterprise Magazine Team ·
RAG that works in a pilot can turn slow, costly and leaky at enterprise scale. Experts from Tecton, Nexla and Novartis explain how to cut RAG costs, keep indexes in sync, enforce chunk-level access control and evaluate accuracy honestly.
By The AI Enterprise Magazine Team ·
AI models for chemistry often fail on compounds no one has tested because training data is scarce. SandboxAQ's Ang Xiao says large quantitative models add physics-based synthetic data, citing battery life prediction, paper mining, materials discovery and formulation.
By The AI Enterprise Magazine Team ·
Getting a model working in a notebook is easy; keeping it reliable in production is not. Leaders from Tecton, Emory Healthcare and a BBDO agency say standardization, cross-training, unified frameworks and clear ownership speed the path to production AI.
By The AI Enterprise Magazine Team ·
Should enterprises host their own LLMs, use cloud models or run small models on laptops? Engineers from SandboxAQ, Alteryx and Plotly weigh the tradeoffs and explain how governed access, testing and open frameworks help future-proof AI infrastructure.
By The AI Enterprise Magazine Team ·
Payment processor North replaced a third-party fraud detection product with its own real-time machine learning system. MLOps leader Ben Orkin explains the build-vs-buy decision, the data signals that matter and how a feature store cut model iteration from months to weeks.
By The AI Enterprise Magazine Team ·
Rafay offers self-service GPU access, AnswerRocket a conversational analytics assistant, Crowdbotics AI-generated product requirements and code, and DataRobot prebuilt AI apps with real-time guardrails: four approaches to putting generative AI to work.
By The AI Enterprise Magazine Team ·
Relational deep learning can treat a Snowflake database as a graph, says Kumo AI co-founder Hema Raghavan. A predictive query language and graph neural networks replace the hand-written feature pipelines behind churn, recommendation and forecasting models.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, two HPE speakers said deployment, not training, had become the main hurdle for enterprise LLMs. They grouped the problems into technical, operational and security challenges and demoed HPE's inference software, including autoscaling and canary rollouts.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, Vespa.ai's Jon Bratseth and RavenPack's Aakarsh Ramchandani said production RAG needs more than a vector database. They covered hybrid search, cross-encoder reranking, evaluation sets and how to keep embedding and storage costs in check.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by HPE, AI Solution Engineer Tyler Britten demonstrated how to serve an open model on premises, build RAG over documents and databases, and prepare a prototype for production with testing and evaluation.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by HPE, leaders from HPE, LTK and the University of Texas Medical Branch discussed how generative AI is reshaping computer vision. They covered multimodal RAG, synthetic data, production lessons, governance and data challenges.
By The AI Enterprise Magazine Team ·
At COLLIDE 2024 in Atlanta, Informatica's Peter Manta said AI projects succeed or fail on data. He offered a three-part test of robust, relevant and responsible data, and urged teams to involve the data office early and pilot small.
By The AI Enterprise Magazine Team ·
In a COLLIDE 2024 session, Esri's Brady Cline argued that location is an underused ingredient in enterprise analytics. He outlined business questions spatial data can answer and showed a recorded demo of GeoAnalytics Engine analyzing millions of customer records inside Spark.
By The AI Enterprise Magazine Team ·
In a COLLIDE 2024 session, Vespa.ai's Kristian Aune ran a classification experiment using retrieved examples and a language model, reaching 94% accuracy in half a day. He compared text and vector search, model sizes and example counts, and described a data flywheel.
By The AI Enterprise Magazine Team ·
At COLLIDE 2024 in Atlanta, Snowplow co-founder Yali Sassoon said AI advantage comes from feedback loops that generate proprietary data. He described Klarna's support agent as an example and listed five ingredients for building a data flywheel.
By The AI Enterprise Magazine Team ·
How do you open data to non-technical employees without exposing sensitive information? Leaders from Piedmont, The Home Depot, Alteryx and BBDO point to certified data sets, business-owned data hubs, time-limited PII access and a break-the-glass log for health data.
By The AI Enterprise Magazine Team ·
In a COLLIDE 2024 session, WhereScape's Patrick O'Halloran compared small, mid-sized and large companies building data foundations in the cloud. He recommended narrow scope, rapid prototyping and treating the work as a business process, with automation to cut testing and rework.
By The AI Enterprise Magazine Team ·
AI strategies fail when the data underneath them isn't right. Leaders from Southern Company, Clorox, Morgan Stanley and Tecton explain how to tie data strategy to business value, break down silos with standardized features, and scale pilots that break at full volume.
By The AI Enterprise Magazine Team ·
AI ROI is won or lost at every stage of a project. Leaders from Bank of America, Carelon Health, Lockton and PRGX break down business cases, the three E's of measuring value, data and training bottlenecks, and how unmonitored cloud and GPU spending erodes returns.
By The AI Enterprise Magazine Team ·
At COLLIDE 2024 in Atlanta, Pure Storage's Chad Hendren said AI pilots often break when demand grows, because storage, metadata and power limits start to bite. He shared his company's claims on parallelized file tools, flash density and customer results.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, leaders from Snowplow, SQream and Tecton discussed how generative AI complements machine learning in executive decision-making. They covered unstructured data, evaluation, ROI, KPIs and the build-versus-buy choice.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, leaders from Deloitte, Monte Carlo and PRGX Global explained why data quality and observability underpin generative AI. They covered the five pillars, how to start, what to look for in tools and common pitfalls.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, panelists from SQream, Domo, Snowflake and Matillion explained why data lakehouses suit generative AI. They covered migration and interoperability, avoiding a data junk drawer, governance, GPU costs and where architectures are headed.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by Astronomer, speakers from Astronomer and AWS described how Apache Airflow orchestrates RAG, fine-tuning and monitoring for LLM applications. They shared customer examples and two internal Astronomer tools.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, executives from Informatica, Graft, Metaphor and Snowflake discussed how generative AI can widen access to data. They said semantic context, data quality, permissions and focused first use cases decide whether it succeeds.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar made possible by DataRobot, data science practitioners discussed feature engineering for predictive models. They said the biggest obstacles are organizational, and that generative AI helps most with scarce or unstructured data when paired with expertise and risk controls.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar brought to you by Satori, leaders from Strata Decision Technology and Satori explained how to grant self-service access to sensitive healthcare and financial data. They stressed automation, access lanes, dynamic masking and tying permissions to tickets.
By The AI Enterprise Magazine Team ·
In a January 2024 AI Connect webinar, experts from Esri and AWS explained how to run spatial analysis natively in a Spark and Amazon EMR environment. They covered common pitfalls, available functions and a Chicago ride share demand demo with more than 50 million records.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Zilliz developer advocate Yujian Tang explained why vector databases matter for AI applications. He covered retrieval augmented generation, semantic similarity, and why SQL databases and search libraries fall short at scale.
By The AI Enterprise Magazine Team ·
In a keynote at COLLIDE 2023 in Atlanta, Stardog VP of Product Navin Sharma argued that enterprise knowledge graphs give generative AI the business context it lacks. He said the pairing can improve accuracy, cut hallucinations and make answers explainable.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, RelationalAI's Nikolaos Vasiloglou explained why cloud data platforms with hundreds of millions of columns need a semantic layer. He described how ontologies, human review and knowledge graphs help language models answer questions.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, Redis principal solution architect Anton Umnikov explained how transformers led to embeddings and ChatGPT, and how a RAG system fits together. His advice: pick embeddings, vector database and LLM independently, and remember that vectors are easy while databases are hard.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Cox Automotive's John Kang described how Manheim moved capacity planning from Excel to a Python app built with Dash Enterprise and Plotly Professional Services. The tool helps 50-plus auction sites plan technicians and equipment through scenario planning.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, Graft CEO Adam Oliner said a good AI strategy must use all of a company's data, support experimentation, lift the whole organization and empower everyone. His conclusion, from a vendor's perspective, was to adopt a modern AI platform rather than build one.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Neo4j's Phani Dathar said knowledge graphs can address LLM inaccuracy and missing company knowledge. He described a connect, enrich and consume approach that pairs vector search and generated graph queries with an LLM.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Dremio's Jeremiah Morrow demonstrated how branching in the Dremio Arctic Iceberg catalog gives data scientists isolated, zero-copy clones of production data. The demo showed a branch, a tagged change, a merge to main and a one-command rollback.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Vida Williams of the Virginia Alcoholic Beverage Control Authority looked back on 25 years in data and enterprise architecture. She drew lessons on data modeling, executive attrition, data-driven culture and the slow ROI of data, then cautioned against tech for tech's sake in generative AI.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Redis Field CTO Allen Terleto argued that data platforms are converging and that vector search is the newest capability vendors are adding. He explained how RAG and semantic caching can reduce LLM cost, latency and data risk, and what enterprises should weigh when choosing a vector database.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, dotData CEO Ryohei Fujimaki argued that generating features automatically from raw business tables is faster and more reproducible than hand-built features. He also described three ways generative AI is being applied to unstructured text, conversational feature creation and feature interpretation.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Intuit Factory founder Roop Singh argued that the winners in enterprise technology will combine AI, blockchain and data. He predicted priced data marketplaces and cheaper models that let small firms compete.
By The AI Enterprise Magazine Team ·
In a session sponsored by Lenovo at COLLIDE 2023 in Atlanta, Mike Leach argued that AI teams should match hardware to the workload and treat workstations as an on-ramp to the data center. He also said IT, data science and finance need to plan AI spending together.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, dotData's Ian McKenna demonstrated a way to turn disjointed tables into a flat, model-ready feature table. He argued for letting the data surface features statistically, then reviewing them with experts.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Stardog knowledge graph consultant Joe Fagan built a semantic layer live on the royal family tree. He showed how ontology rules and inference add facts that never appear in the source data.
By The AI Enterprise Magazine Team ·
In a Data Science Innovators Conference 2023 keynote, Stardog's Navin Sharma said metrics layers capture only part of what a semantic layer can do. He argued a knowledge graph-powered layer that federates data, infers relationships and keeps business meaning can unify hybrid multicloud data.
By The AI Enterprise Magazine Team ·
In a panel at the Data Science Innovators Conference 2023, data leaders from Kellogg, Travelers, ADP, Honeywell and Stardog discussed how to evaluate generative AI and knowledge graphs. They covered LLM privacy trade-offs, failure modes, ROI methods and governance.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Redis' Talon Miller explained why running Redis on Kubernetes is hard and how the Redis Enterprise operator automates it. He also described claimed availability and speed gains and previewed Redis on Flash and active-active deployment.
By The AI Enterprise Magazine Team ·
In a workshop at the Data Science Innovators Conference 2023, Redis professional services leaders explained how Redis Enterprise handles clustering, failover and sharding, and how developers use its data structures, JSON and search. Live demos showed hashtags for multi-key commands and faceted product search.
By The AI Enterprise Magazine Team ·
In a hands-on workshop at the Data Science Innovators Conference 2023, Starburst's Matt Fuller and Tom Nats explained how Trino and Apache Iceberg combine into an open lakehouse. A live demo showed merges, updates, deletes and snapshot time travel.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Navin Sharma of Stardog showed how a knowledge graph can infer relationships through domain rules, using a royal family dataset and demos in manufacturing, life sciences and finance. He also cited vendor ROI figures and answered questions on ontologies and Microsoft Purview.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Taimur Rashid of Redis argued that leaders should accelerate digital innovation while building systems that outlast them. He covered vector databases, customer examples from Capital One and Dick's Sporting Goods, and security, sustainability and inclusion.
By The AI Enterprise Magazine Team ·
In a hands-on workshop at the Data Science Innovators Conference 2023, Stardog co-founder Mike Grove built a customer 360 knowledge graph live. He covered data modeling, virtual mapping of source data, and an inference engine that derives new relationships.
By The AI Enterprise Magazine Team ·
In a hands-on session at the Data Science Innovators Conference 2023, Ontario Tech University students Benjamin Fedoruk and Max Sun introduced APL for data science. They covered array operators, right-to-left evaluation, tacit functions, and a Jupyter and Python demo that plotted social media emotion data.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Intel Chief Data Scientist Dr. Melvin Greer described how web3 infrastructure and the metaverse could move the web from consumption to creation. He urged AI practitioners to start experimenting with blockchain, NFTs and cryptocurrencies.
By The AI Enterprise Magazine Team ·
