Top 5 AI considerations for chief data and analytics executives

Hybrid cloud is the next frontier for scaling enterprise data science, and it’s breaking down the silos between on-premises and cloud environments to unlock the considerable benefits of each. To help data and analytics executives design their AI/ML stacks for the hybrid cloud future, Domino Data Lab, NVIDIA, and NetApp worked with ISG Research to identify five key considerations:

• Scaling AI with MLOps/LLMOps and GPUs: MLOps/LLMOps platforms and GPU acceleration can help you get more cutting-edge models into production quicker.
• Managing distributed data on-premises and in the cloud: The hybrid cloud is here to stay for organizations working with massive amounts of disparate, distributed data.
• Harnessing data gravity with a hybrid cloud strategy: Keep data moving freely within data residency and data sovereignty regulations.
• Simplifying AI/ML governance with hybrid MLOps/LLMOps: Hybrid cloud architectures let organizations lower costs and improve operational efficiency.
• Future-proofing AI strategy with a scalable data science platform: Supporting the hybrid cloud enterprise IT strategy balances openness, agility, and flexible compute.