What’s next in enterprise AI?
Episode 3 of The hidden future of AI series
Watch on demand
The transformational power of AI is undeniable, but many enterprises struggle to scale amidst infrastructure complexity, regulatory pressures, and talent shortages. Organizations must navigate fragmented environments, rising cloud costs, and the need for robust governance.
In this session, Domino Data Lab and NetApp explore key challenges in AI adoption, including unifying data and model pipeline management and compliance.
We’ll also discuss::
- Overcoming infrastructure fragmentation to enable seamless AI scaling.
- Addressing governance and compliance in highly regulated industries.
- Bridging the gap between data science and IT to drive operational efficiency.
- Predictions on unifying data, AI, and infrastructure to drive agility and long-term growth.
Featured speakers
Richard Swakla
Data centric workloads specialist
Richard Swakla is a seasoned technology leader with a proven track record of helping enterprises accelerate AI/ML pipelines, enhance data scientist productivity, and build high-performing hybrid cloud architectures. With deep expertise in aligning IT strategies with business objectives, Richard specializes in breaking down data silos, streamlining global AI deployments, and enabling Retrieval-Augmented Generation (RAG) systems for secure and efficient business value. Passionate about driving transformational change, he combines a focus on cutting-edge technologies like big data analytics, machine learning, and cloud computing with a commitment to helping clients achieve their goals efficiently and effectively.
Dr. Kjell Carlsson
Head of Data Science Strategy, Domino
Dr. Kjell Carlsson advises organizations on how to drive business outcomes with artificial intelligence (AI) and data science. Previously, he covered AI, ML, and data science as a Principal Analyst at Forrester Research where he wrote reports on topics ranging from computer vision, MLOps, AutoML, and conversation intelligence to augmented intelligence, next-generation AI technologies, and data science best practices.