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Ep 85 | Enterprise AI Success: What Separates Results from Expensive Experiments

Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear? For Mark Ritcey, the answer comes down to disciplined execution. AI programs need a clear business problem and an organization prepared for how the technology changes the way work gets done. In this episode of The AI Forecast, Paul Muller sits down with Mark Ritcey, Vice President of AI and Automation Delivery at Latentbridge and lecturer on AI and machine learning, to examine the decisions that shape enterprise AI success.

Avoid the Cloud Token Trap & Secure Your AI | Cloudera AI Inference Service Demo

Relying strictly on public cloud AI APIs often leads to unpredictable token costs and data residency concerns. In this demo, Peter Ableda (Product Lead for Cloudera AI) demonstrates how the Cloudera AI Inference service enables enterprise-grade private AI behind your firewall—whether on-premises or in a Virtual Private Cloud (VPC). Learn how bringing AI directly to your data ensures total model control, cost predictability, and maximum data security.

How Automating Marketing Data with AI Saves Hours

Did you know Cloudera's marketing operations team is using AI to automate workflows and eliminate manual processes? In this video, Kelly Sutter shares how Cloudera empowers marketing teams to unify data, uncover customer insights faster, and optimize marketing analytics with trusted enterprise AI. Discover how bringing AI to your data makes marketing more connected, responsive, and effective.

Bring AI to Your Data, From Prototype to Production | Cloudera AI Inference

Most enterprise AI projects stall when teams try to move experiments into production—where costs, governance, data security, and scale all get real. In this demo, see how Cloudera AI Inference helps turn foundation models into secure, governed, production-ready AI services. You’ll learn how to: Chapters: Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations.

Meetup - AI Lakehouse

Watch deep-dive presentations and live demos from industry experts as they unpack next-generation data frameworks built from the ground up for multimodal AI, autonomous data agents, and cross-cloud architectures. Key Technical Highlights Inside: AI Lakehouse Meetup - Bay Area 15th July 2026 Cloudera SanJose Office A must-watch technical guide for data engineers, platform architects, and MLOps teams!

Pharmaceuticals and Biopharma Clinical Trials Optimization with Cloudera

How AI is transforming clinical trial operations—without compromising critical data. Biopharmaceutical companies are routinely slowed down by siloed datasets and complex workflows. While artificial intelligence promises to accelerate drug development, clear up decision-making opacities, and build dependencies, handling highly sensitive medical data requires ironclad protection.

Ep 83 | From KPIs to Action: What Comes After The Dashboard?

Your dashboard can tell you sales are down, but it can't tell you why or what to do next. Dashboards have become the default way to monitor a business. Bhaskar Sunkara argues they're only the starting point. The next step is AI that understands business context and helps leaders move from insight to action.

Ep 80 | Decision Logic: The Difference Between an Answer and a Decision

Ask an AI system a question, and you'll get an answer. Decision logic determines whether you should trust it. In this episode of The AI Forecast, Paul Muller sits down with Darlene Newman, Innovation Lead at Duczer East, to explore the hidden layer that helps AI move from pattern matching to practical decision-making.

Is Your AI Startup CEO Lost in the Plot? #Shorts #podcast #cloudsecuritypodcast

Drawing on his experience at Google, Google X, and as the CEO and co-founder of an AI startup, Varun Puri shares practical lessons on embedding AI into everyday workflows and building habits that stick. Discover how to maintain perspective on what is working, even when AI constantly highlights what isn’t.