Atlanta Apache Spark User Group

Data democratization promises to empower organizations with seamless access to insights, but many enterprises stumble on the road to realization.

In this webinar, industry experts dissect the root causes of these challenges, from cultural resistance to technical bottlenecks. Gain actionable strategies to bridge the readiness gap and unlock the full potential of democratized data.

You’ll Learn:

• Understanding Failure Points: Why data democratization initiatives often falter despite robust investments.
• Addressing Readiness Factors: Key readiness factors organizations must address to enable a culture of data empowerment.
• Overcoming Common Challenges: Practical approaches for overcoming cultural, structural, and technical hurdles.

Panelists to be announced soon

Text is only one piece of the data puzzle—images, videos, and other modalities hold massive untapped potential. But scaling multi-modal AI systems that can process, retrieve, and rank these rich data sources is a complex challenge.

In this webinar, we’ll dive into the latest breakthroughs in multi-modal AI at scale. Using Vespa.ai as a backbone, we’ll explore architectures and retrieval strategies that unify text, images, and video into a seamless, high-performance search and discovery experience. Learn how to blend embeddings, vector search, and dynamic filtering to serve diverse content efficiently, unlocking new opportunities for recommendation, moderation, and knowledge extraction.

Key Takeaways:

• Multi-Modal Architectures: Understand how to integrate text, images, and video embeddings into scalable AI systems for powerful search and retrieval.
• Real-Time Performance: Discover strategies for ensuring low-latency retrieval without sacrificing accuracy or relevance, even with high data volumes.
• Dynamic Filtering & Ranking: Learn best practices for adaptive filtering and ranking to serve personalized and context-aware results.

As AI systems continue to scale, observability has become an essential component for success. This webinar explores two key dimensions: data observability, which focuses on monitoring and improving the quality of data that drives AI systems, and AI observability, which provides transparency and accountability for model performance and decision-making.

Learn practical strategies for building "glass box" AI systems where insights are clear, issues are easy to diagnose, and scalability is seamless.

Key Takeaways:

• Understand Data Observability: Learn how to implement monitoring systems to ensure high-quality, reliable data pipelines for AI systems.
• Explore AI Observability: Discover tools and techniques for monitoring AI models, ensuring they are transparent, unbiased, and explainable.
• Bridge the Gap: Integrate data and AI observability practices to achieve robust, scalable, and trustworthy AI deployments.

Panelists to be announced soon