Companies are increasingly becoming data-driven by capturing market and environmental data, using analytics and machine learning to identify complex patterns and detect change, and making predictions that directly impact the bottom line. Being data-driven is an essential part of any leading industry today. As a result, modern data-driven organizations must manage a deluge of data. And the rate at which we continue to generate data is growing exponentially with the widespread adoption of sensors - from video cameras and smartphones to manufacturing sensors and more.
Notable examples include:
- Accelerate AI transformation and associated AI ROI.
- Increasing productivity in data science and reducing the time required to prototype, train, test, and deploy models at scale.
- Build industrialized, mechanized AI workflows that enable "MLOps" in a modern enterprise environment.
- Reducing time-to-insight with infrastructure built on NVIDIA platforms (DGX, EGX).
MARKET DYNAMICS
The availability of open source data analytics and machine learning software at scale in the mid-2000s sparked the big data revolution. Large companies across huge industries, including retail, finance, healthcare and logistics, adopted data analytics to improve their competitiveness, responsiveness and efficiency. Over time, they've discovered that a few percentage points can add billions of dollars to the bottom line. Companies can leverage new AI technology in speech recognition and natural language processing for speech-to-text and translation tools, recommendation systems to improve the shopping experience and content consumption, and improved supply chain management from warehouse to store.
To draw insights from this data, organizations need significant computing power and the right tools and algorithms for their teams, from IT to developers. And today, more than ever, executives need to transform their businesses while ensuring positive ROI and scalable infrastructure.
Every business needs to transform with the power of AI to improve customer relationships, streamline supply chains and deliver better patient outcomes. While most companies know they must invest in AI to secure their future, many are struggling with the strategy and platform that can enable success.
As the pace of AI investment increases, more companies are finding themselves creating development silos across their organization that are deployed outside of IT and drive CapEx/OpEx. This proliferation of "shadow AI" hinders talent development and sharing of best practices, consumes resources at an exponential rate, and delays time to insight. This approach, often led by developers outside of IT, is not optimized for enterprise scale and lacks integration with the DevOps culture and approach that drives most modern enterprises.
Without an approach that meets the needs of today's enterprises, many companies could find themselves escalating data science investments and infrastructure spending, making the ROI of artificial intelligence even more elusive.
Unlike traditional enterprise applications, AI is still quite young. It is anchored in rapidly evolving open source and bleeding-edge code and lacks proven approaches to meet the needs of the enterprise in a scaled production environment. Simply put, AI can be complex to develop, difficult to scale, and brittle to deploy. AI workloads need a new platform that is optimized for enterprise and supports the entire implementation lifecycle - from experimentation and prototyping to model training at scale and deployment in production.
NVIDIA has more than a decade of experience building AI for organizations around the world, including some of the world's largest hyperscale environments. Through this experience and the expertise of a team of over 14,000 AI experts, NVIDIA has developed an end-to-end platform that enables any organization to realize its AI-driven ambitions, with an industrialized workflow that spans from development to deployment and from concept to insights.
With a proven end-to-end architecture from NVIDIA, enterprises can succeed with AI:
- Enables data scientists and developers to focus on productive experimentation and prototyping instead of software engineering, system integration, and troubleshooting. Ready-to-use software stacks from the NVIDIA NGC Software Hub are fully optimized so practitioners can start earlier, experiment and iterate faster, and achieve the highest model accuracy with the least effort.
- Help infrastructure teams implement "MLOps," the convergence of AI development and DevOps approaches. In doing so, they can achieve an enterprise-level AI development and model training workflow that is industry-ready, streamlined, and built on NVIDIA DGX - the world's first portfolio of AI supercomputers designed specifically for training the most complex models in the data center.
- Preparing deployment teams to bring production-ready models online and supporting inference at scale with NVIDIA EGX. The EGX platform enables organizations to easily deploy and manage AI workloads at the edge or in a remote data center. It includes NVIDIA Cloud Native, Edge First, and a scalable software stack for accelerating AI workloads at remote sites.
This solution combination provides enterprises with a robust, secure end-to-end platform that supports the develop-to-deploy lifecycle of AI implementation, while also being optimized for business and supported by the world's largest team of AI experts.
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