Go Back Up

April 17th 18:00-19:30 BST

AI & Data: powering the next era of innovation

This exclusive virtual roundtable brings together senior executives to discuss the intersection of AI and data, where cutting-edge analytics meets intelligent automation. As organizations strive to harness vast data ecosystems, challenges around governance, real-time processing, and ethical AI loom large. Join us for a deep dive into how leaders are overcoming these hurdles and transforming data into actionable intelligence.

This roundtable brings together data scientists, engineers, and AI practitioners who are actively working on building, optimizing, and deploying data-driven solutions.

Expect to connect with experts tackling real-world challenges in AI integration, machine learning models, data pipelines, and cloud scalability - sharing insights that can shape the future of intelligent systems.

 

 

Who Will Be There?

This roundtable brings together data scientists, engineers, and AI practitioners who are actively working on building, optimizing, and deploying data-driven solutions.

Expect to connect with experts tackling real-world challenges in AI integration, machine learning models, data pipelines, and cloud scalability - sharing insights that can shape the future of intelligent systems.

 

 

What's The Agenda?

This virtual roundtable is designed for hands-on data professionals looking to dive deep into the technical and practical aspects of AI and data integration. The session will be a highly interactive discussion, where attendees can share experiences, explore challenges, and gain actionable insights from peers working on real-world AI and data solutions.

Key discussion points include:

  • Bridging AI and Data Pipelines: What are the most effective ways to integrate AI models with real-time and batch data processing systems?

  • Optimizing Data Infrastructure: How do you ensure your data architecture is scalable, cost-effective, and ready for AI-driven workloads?

  • Improving Model Performance: What techniques are you using to fine-tune machine learning models and ensure they generalize well in production?

  • Automation & MLOps: How can teams streamline model deployment, monitoring, and lifecycle management to reduce technical debt?

This session is an opportunity to collaborate with other experts, gain new perspectives, and walk away with fresh ideas to apply in your own work.

Register Here