What's new and what's next for Red Hat AI: Your path to enterprise-ready AI | Q4 2025

Описание: Join us for a special Q4 AI product update session of What's New and What's Next, featuring Red Hat AI’s leaders, Joe Fernandes, Steven Huels and the Red Hat AI Product Management team.

In this session, you'll learn about:
Latest advancements in Red Hat AI: Discover how our latest releases help you address challenges like cost, complexity, and control.
High-performance, predictable inference: Learn how to deploy any model on any hardware, and any cloud, with built-in observability to track and meet Inference SLOs.
Accelerated AI agent development: We'll showcase features that streamline AI agent development and deployment.
Key new features: Simplify connecting models to your enterprise data, and provide a vendor-neutral platform for managing AI-token-based workloads.
The future of Red Hat AI: Get a sneak peek into what's next for our portfolio.

💡 Learn more about Red Hat AI 3: https://www.redhat.com/en/products/ai
🔗 Link to slides: http://red.ht/ai-whats-new-whats-next...

00:00 - Red Hat AI 3 overview with Joe Fernandes and Steven Huels
03:01 - Fast, flexible and scalable inference
04:46 - Red Hat AI Inference Server & Red Hat AI Model Repository
06:05 - What makes model workloads unique?
07:21 - llm-d reimagines how LLMs run on Kubernetes
08:00 - Model-as-a-Service in OpenShift AI
08:42 - Accelerate agentic AI innovation
10:46 - Connecting models to data with a modular approach
12:46 - Scaling AI across the hybrid cloud with the Red Hat AI platform
14:53 - Red Hat AI 3 features across the four pillars summary slide and closing from Joe Fernandes
15:33 - What’s new, what’s next Q4 2025 edition introduction and Red Hat AI overview with Tushar Katarki
19:02 - Red Hat AI 3 announcements overview with Jeff DeMoss
22:16 - Enterprise GenAI inference platform with Erwan Gallen
23:54 - Inference server deep dive: connecting models to the hardware
26:29 - Models deep dive with Rob Greenberg
35:54 - Distributed inference framework with llm-d deep dive with Naina Singh
39:51 - Model-as-a-Service in OpenShift AI deep dive with Adam Bellusci
43:04 - Connecting models to data with Jehlum Vitasta Pandit
47:30 - Synthetic data generation hub with Aditi Saluja
50:49 - Introduction to Training Hub
52:15 - Introduction to Continual post training
55:23 - Evaluation with William Caban
57:13 - RAG with Llama Stack & RAG highlights
59:58 - Kubeflow Trainer with Christoph Gorn
1:04:18 - Red Hat AI Feature Store with Kezia Cook
1:06:46 - Introduction to its-hub with Luke Inglis
1:07:54 - Agentic AI focus areas with Adel Zaalouk
1:19:56 - Generative AI in OpenShift AI 3.0 with Jenny Yi
1:21:32 - Introducing the Gen AI Studio with Peter Double
1:22:52 - AI Platforms with Adam Bellusci
1:25:51 - AI Pipelines with Myriam Fentanes Gutierrez
1:30:51 - AI Safety and Guardrails highlights with Adel Zaalouk
1:34:53 - Introducing Workbenches 2.0 with Kezia Cook
1:37:00 - GPU-as-a-Service with Christoph Gorn
1:41:15 - OpenShift AI dashboard 3.0 with Jenny Yi
1:43:40 - Red Hat AI Accelerator support with Erwan Gallen
1:46:17 - What’s new and next with Red Hat AI Inference Server
1:47:10 - OpenShift AI Roadmap preview with Jeff DeMoss
1:47:53 - Lifecycle updates and closing remarks with Tushar Katarki