Atlanta Apache Spark User Group

AI agents are only as effective as the data they can access. Yet most enterprise architectures still depend on batch pipelines, fragmented systems, and tightly controlled operational environments that limit real-time context.

In this technical deep dive, Striim and Google Cloud outline a practical, production-ready architecture for streaming real-time operational data into Google Cloud to power AI at scale.
We’ll examine how low-latency Change Data Capture (CDC) enables continuous movement of trusted enterprise data from on-prem, hybrid, and multi-cloud environments into BigQuery and Cloud Storage—creating a dynamic foundation for analytics, model training, evaluation, and serving on Vertex AI.

The session goes beyond reference diagrams. We’ll explore how continuously refreshed data supports goal-directed reasoning in AI agents, improves memory retention through up-to-date context, and enables dynamic planning in real-world operational workflows. Just as importantly, we’ll address governance, observability, and architectural flexibility—so teams can modernize without compromising compliance or performance.

If you're building AI systems that need fresh enterprise context to act safely and effectively, this session will give you a blueprint grounded in real-world constraints.

What You'll Learn:

1️⃣ Real-Time Data as Agent Context: How low-latency CDC pipelines create continuously updated data products that power training, evaluation, and real-time inference on Vertex AI.

2️⃣ Architecture for Hybrid and Multi-Cloud Enterprises: Patterns for securely streaming operational data from on-prem and distributed systems into BigQuery and Cloud Storage without disrupting core systems.

3️⃣ From Data Movement to AI Activation: How fresh, governed data enables agent memory, dynamic decision-making, and modular AI workflows at scale.

4️⃣ Governance, Observability, and Control: How to maintain compliance, enforce access controls, and monitor data pipelines while supporting production-grade AI systems.

5️⃣Future-Proof Platform Design: How to architect for flexibility—so your AI stack can evolve as models, agents, and enterprise requirements change.

This is an architecture-led session for data and AI leaders who want to move beyond experimentation and build enterprise AI systems that operate in real time—securely, reliably, and at scale.

Experimentation is easy. Real-world deployment? That’s where the infrastructure proves its worth.

In this session, we’ll go beyond prototypes and pilot programs to explore what it really takes to build AI infrastructure that holds up under production demands. From GPU scheduling and model orchestration to observability, governance, and scaling compute across hybrid environments—this discussion is for teams ready to move past the lab and into sustained delivery.

Our panel of leaders will break down architectural trade-offs, emerging best practices, and the organizational realities of supporting production-grade AI.

What You'll Learn:

1️⃣ Foundational Stack Components: What your AI infrastructure needs to support scale, speed, and stability in production.

2️⃣ Deployment Realities: Insights into latency, availability, compliance, and lifecycle management when shipping AI to production.

3️⃣Infrastructure as Collaboration: How infra, ML, and product teams can align on goals, budgets, and tooling without bottlenecks.Engage live with founders and builders shaping the future of autonomous enterprise solutions.

Panelists to be announced soon.

Explore how to develop and deploy Enterprise AI applications with Mistral AI in this practical, insight-driven session. We’ll walk through Mistral’s full model spectrum—from Large to Small—and how to select the right capabilities for your specific use case.

You’ll get an introduction to Mistral AI Studio and learn how to begin building, customizing, and managing AI applications with speed and control. The session will also cover flexible deployment pathways, including serverless, cloud, on-premise, Cloud-VPC, and dedicated instances—so you can align infrastructure with enterprise requirements.

What You’ll Learn:

🔹 Model Selection Strategy: Understand the Large-to-Small model spectrum and when to use each.
🔹 Application Development: Build and tailor AI applications in Mistral AI Studio.
🔹 Flexible Deployment Options: Compare serverless, cloud, on-prem, VPC, and dedicated setups.
🔹 Production Best Practices: Apply practical guidance to move AI projects into enterprise-scale production.

Gain the clarity and tools needed to take AI initiatives from experimentation to reliable, production-ready systems.

Forget static demos and slide decks. Plug & Play Sessions are all about showing, not telling. In this hands-on webinar series, you'll see tech leaders walk through their AI product in real time, highlighting exactly how it works in the wild: setup, workflows, integrations, all in just 30 minutes.

Built on the momentum of our AI Demo Day format, Plug & Play goes deeper. Presenters slow down and dig in: demonstrating usability, showing off speed to value, and guiding attendees through common real-world scenarios. It’s a rare opportunity to see how tools behave under actual conditions, not just polished slides.

Expect 2–3 hours of back-to-back, engaging walkthroughs. Attendees are encouraged to click along live, ask questions, and walk away with more than awareness—usable knowledge.

Key Takeaways:

1️⃣ Real Product Experience: See full workflows in motion—not just the highlights.

2️⃣ Usability in Focus: Evaluate ease of use, integration paths, and how quickly value is delivered.

3️⃣ Interactive Learning: Follow along live and get answers as you go.

4️⃣ Depth Over Demos: A deeper understanding of how AI tools actually fit into your stack.