Virtualize GPU-accelerated Data Science and AI Workflows in Your Data Center with Enterprise MLOps
, Senior Technical Product Manager , NVIDIA
, Senior Partner Solution Architect, Domino Data Lab
GPU acceleration brings great promise to data science and AI workloads, but not without challenges. While MLOps can bring the benefits of collaboration and self-service infrastructure to data science teams and AI practitioners, the complexity of integrating AI workloads with existing infrastructure is frequently cited as a top barrier to AI adoption and business impact. Combining Domino Data Lab and NVIDIA-accelerated computing enables companies to cost-effectively scale data science by accelerating research, model development, and model deployment on mainstream accelerated servers. Data scientists can focus on research instead of DevOps by launching environments on demand, with docker images configured with the latest data science tools, frameworks, and optimized accelerated compute — with automatic storing and versioning of code, data, and results. IT can have the confidence of enterprise-grade security, manageability, and support within a familiar environment. Learn how to cost-effectively scale data science!