whitepaper

Top 5 AI Considerations for Chief Data and Analytics Executives

Accelerate Enterprise Data Science in the Hybrid Cloud with MLOps

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 benefits of each, all while improving collaboration and regulatory compliance.

Model-driven companies that are out-innovating their competitors with machine learning and AI are adopting hybrid cloud strategies across data and analytics initiatives, running data science workloads where they make the most sense based on cost, performance, and regulatory considerations.

Next-Generation AI Infrastructure for the Hybrid Cloud Future

Data and analytics executives have difficult decisions to make, working cross-functionally with IT and the business to maintain:

  • Modern Platform for Top Talent: Top performing data science experts demand cutting-edge tools and infrastructure to do their best work, along with seamless access to data.
  • Less Risk from Better Compliance: Ensure customer trust and regulatory compliance with consistent processes, centralized infrastructure, limited access to sensitive data, and adherence to data residency and data sovereignty regulations.
  • Greater Business Impact: Increase the volume and quality of data science models that inform new offerings, improve customer experiences, and increase profit.

That’s why Domino Data Lab and NVIDIA worked with David Menninger, SVP & Research Director of Ventana Research, to identify five key considerations that data and analytics executives should have in mind while designing their AI/ML stack for the hybrid future.


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Here’s a sneak peek at the considerations

Scaling Data Science with MLOps and GPUs

MLOps platforms and GPU acceleration can help you get more cutting-edge models into production quickly, but siloed data and infrastructure can disrupt the entire lifecycle.

Managing Distributed Data On-Premises and in the Cloud

Hybrid is here to stay, with organizations working with massive amounts of disparate, distributed data across on-premises, cloud, and multi-cloud environments.

Harnessing Data Gravity with a Hybrid Cloud Strategy

Data residency and data sovereignty regulations often prevent the movement of data for data science, while also making it difficult to predict infrastructure costs.

Simplifying AI/ML Governance with Hybrid MLOps

Hybrid MLOps architectures allow an organization to more easily realize the benefits of on-premises and cloud infrastructure, lowering costs and improving operational efficiency.

Future-Proofing AI Strategy with a Scalable Data Science Platform

Organizations that adopt an MLOps platform supporting their hybrid enterprise IT strategy to balance openness, agility, and flexible compute - regardless of data location - will gain strategic advantage.

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