Many organizations embarking on AI projects quickly come to realize that a strategic commitment to data science expertise isn’t enough to create production-ready apps that can crank out insights from data.
NVIDIA is working with Digital Realty and Core Scientific to help customers close this gap and bring more of their AI models into production.
The Growing Model Debt Problem
Businesses often develop valuable models in weeks, only to see them languish as prototypes for months. Why is that?
First, AI models that are designed to solve a business problem aren’t built or deployed like conventional software. Considering the data science sweat equity plowed into all that work, organizations are incurring a growing amount of model debt in terms of investment and resources sunk into undeployed models.
Model development is a complex process with multiple pipelines for data prep, model prototyping, training and inference. It’s done by data science “artisans” whose expertise is in experimentation and algorithms, not software engineering or designing platforms for scalability.
And they’re not just building a single app. It’s a model, a web service and the integration of the two.
In addition, assessing a model’s production-readiness isn’t a simple pass/fail decision. Model accuracy can degrade and drift more rapidly than conventional software, so data scientists must monitor and retrain them continually.
Industrializing AI Development with MLOps and PlatformDigital
The fundamental problem many enterprises wrestle with is how to industrialize their AI development pipeline with an enterprise-grade platform and an IT/DevOps approach, also known as MLOps. Our recently announced offering with Digital Realty and Core Scientific can help these businesses bring more of their models into production.
With Digital Realty’s PlatformDigital running Core Scientific Plexus software for MLOps on NVIDIA DGX A100 systems, we’re improving the AI lifecycle from development to deployment. A highly artisanal process becomes industrialized, accelerated and integrated into standard enterprise IT operations.
This IT-approved infrastructure brings data science and MLOps together in a streamlined, manageable process that enables more models to get produced versus stalled at pilot.
PlatformDigital running on DGX A100 offers purpose-built infrastructure that’s optimized for AI development. It delivers the right resources for each job, whether that’s data analytics, training or inference. And it speeds the iteration cycle so data scientists don’t need to wait for their experiments’ results.
With Plexus AI workflow management tools, enterprises can manage users, datasets, model versions and experiments so that a standard process can be implemented to take models from prototype to production rapidly.
This streamlines the handoff between data scientists and DevOps while ensuring manageability and accountability. It also creates a lifecycle for evaluating models for drift so they can be retrained using new data on an ongoing basis.
The AI PaaS Movement Powered by DGX POD
PlatformDigital is part of a growing movement of managed service offerings that pair DGX A100 infrastructure configured in DGX PODs, combined with MLOps software.
These offerings, part of the DGX-Ready Software program, deliver a complete AI platform-as-a-service that benefits data science teams and enterprise IT. These new models make it easier than ever to tap into the performance, ease of use and productivity that businesses experience on DGX systems, now accessible in a convenient opex model that scales cost-effectively.
To learn more, register to join our live webinar on accelerating business innovation and ROI with AI platform-as-a-service. For more information on accelerating AI with PlatformDigital, visit Digital Realty.