Enterprise data science, machine learning, and AI at scale
Strategies to drive value from AI investments
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Are you on the brink of data- and AI-driven transformation but grappling with how to prioritize investments? You’re not alone.
Many enterprises stand where you are, navigating the complexities of hybrid and multi-cloud environments, data governance, centralized management of AI workloads, and managing AI talent challenges.
Domino and NVIDIA surveyed data science and IT leaders and created this eBook as a beacon to guide you through these complexities.
- Discover why most organizations are still in their infancy when it comes to operationalizing AI at scale.
- Understand the unique data requirements for internal and third-party sources that fuel AI workloads.
- Uncover technical challenges across the model lifecycle attributed to lack of data science tooling and infrastructure.
- Recognize the implications of a divide between data science leaders and IT on AI/ML stack decisions so you don’t underinvest in critical capabilities.
- Understand investment decision implications on your broader analytics, data, and AI/ML talent strategy.
Here's a sneak peek at the takeaways
Hybrid- and multi-cloud is the future
Hybrid is here to stay, with most organizations working with massive amounts of disparate, distributed data across on-premises, cloud, and multi-cloud environments.
Data privacy, sovereignty, and regulation compliance are key
Managing data scientists' access to sensitive data is one of the top concerns for organizations.