Artificial intelligence project deployments donโt always go as planned. Learn how to be among the successes and how AI solutions from HPE and NVIDIA breakthrough the barriers to accelerate AI and enable insight-driven enterprise transformation.
Yet, while interest in AI adoption is clearly growing, many projects still fail to make it into production. A surprisingly low 27 percent of AI apps are successfully deployed today.2 As a result, many enterprises are unable to realize the full value of their data, with 80-to-85 percent of enterprises running into last-mile problems with AI solutions.3 At the same time, the amount of data and devices at the edge continues to grow. In fact, by 2022, 50 percent of data will be created at the edge, and 55 billion devices will be connected.4
Why are enterprises struggling to put successful AI proofs-of-concept into production? Where do the challenges lie? And how can they best be overcome?
Transformation challenges create barriers to speed
Insights begin at the edge, where most data is increasingly created through distributed applications or IoT sensors. To enable real-time insights and action within an edge-to-cloud deployment, the complexity of diverse data types and sources, heterogeneous infrastructure, and multiple networks must be addressed.
Many applications that are critical to enterprise operations โ for example, customer relationship management (CRM) and enterprise resource planning (ERP) solutions โ are often deployed outside the public cloud, because enterprises want the applications close to their proprietary data due to latency requirements, application dependencies, and regulatory compliance concerns. This often leaves enterprises with two different operating models โ one in the cloud and one on-premises โ resulting in higher costs to maintain both.
These disparate deployments also make it more challenging for enterprises to unlock the value of their data. Part of whatโs slowing deployments down is the need to bridge data silos, as well as a need to bridge IT solutions from edge-to-cloud to allow insights to flow freely across the enterprise.
Whatโs needed to successfully accelerate AI adoption
Enterprises must find new ways to leverage their data to create efficiencies and realize better outcomes while maintaining high standards of security and privacy. Requirements in six key areas can help accelerate this process:
- Digital engagement โ Ensure digital connectedness, both internally and externally
- Resiliency โ Maintain data access when and where itโs needed in the face of unexpected downtime.
- Cloud โ Improve agility and scalability with cloud technology and software-as-a-service solutions.
- Process integration โ Ensure AI solutions integrate with business processes.
- Security โ Remote deployments drive the need for increased security without impacting services.
How HPE and NVIDIA and help
Using NVIDIA-Certified Systems, HPE provides flexible AI solution deployment options.
An HPE system that is NVIDIA-Certified brings together NVIDIA GPUs and NVIDIA networking onto HPE server platforms. These systems conform to NVIDIAโs design best practices and have passed a set of certification tests that validate the best system configurations for performance, manageability, scalability, and security. With NVIDIA-Certified Systems, enterprises can confidently choose performance-optimized hardware solutions โ backed by enterprise-grade support โ to run accelerated computing workloads securely and optimally, both in smaller configurations and at scale. These systems are ideal for running the NVIDIA AI Enterprise software suite, which is optimized, certified, and supported by NVIDIA for developing and deploying AI workloads on accelerated servers.
HPE offers a number of flexible deployment options, so you can control how to acquire and consume AI. This includes three solution choices, two of which incorporate HPE GreenLake services:
- On-premises โ Enabling organizations to deliver an AI platform on-premises with unmatched speed and capability to enhance enterprise operations from edge to cloud.
- HPE GreenLake cloud services for machine learning โ Using HPE GreenLake gives you the cloud experience for your AI and ML projects with all the security and control of on-premises IT, enabling you to work and innovate faster.
- HPE GreenLake cloud services for virtual desktop infrastructure (VDI) โ Opting for HPE GreenLake for VDI eliminates the need for costly upfront investments or continuous upgrades while delivering on-demand scalability and security.
Add in the HPE Ezmeral and NVIDIA advantage
Integrating the NVIDIA RAPIDS accelerator for Apache Spark and NVIDIA Triton Inference Server into the HPE Ezmeral unified analytics platform streamlines the development and deployment of high-performance analytics, helping you gain immediate results at lower costs.
Itโs a modern analytics solution that comes to your data wherever it lives. You can unify your data globally and make it available to your analytics teams โ whether the data is at the edge, in an enterprise data warehouse on-premises, a cloud data lake, or on other cloud platforms such as Snowflake. Unique data fabric technology ensures global access to data, which is synchronized regardless of location. Now, with the addition of HPE Ezmeral object store with a native S3 API, only HPE can combine files, objects, event streams, and databases into the same data infrastructure managed by a single filesystem that accelerates time to insights.
A choice in deployment models plus exceptional intelligence and performance
Along with deployment choice, consider these advantages when you choose an AI platform from HPE and NVIDIA:
- Accelerated compute โ Optimized for AI and data-intensive workloads, HPE Apollo 6500 systems and HPE ProLiant DL380 servers are NVIDIA-Certified to run with NVIDIA GPUs.
- Faster time to value โ DevOps and MLOps software supported by a robust ISV ecosystem has been tested for interoperability and functionality that allows applications to be up and get running quicker.
- World-class expertise โ HPE Pointnext Services can help design and implement the right approach for your AI transformation at any point in the AI journey.
Ready to unleash your AI capabilities to enable integrated, impactful, and cost-effective outcomes? Let HPE and NVIDIA help you achieve greater insight on demand through the value of AI.
To learn more about what HPE and NVIDIA are doing to bring AI to the enterprise, read our business paper on the industrialization of AI, or visit our AI solutions and NVIDIA partner pages.
Meet our Tech Insights bloggers
Currently, Katy is the Deputy of the AI Strategy & Solutions group at HPE and leads the team developing the AI Solutions Portfolio. She has held several leadership roles within HPE including overseeing the execution of โThe Machineโ research project in Hewlett Packard Labs as well as in a variety of technology incubation areas. She has an extensive R&D background delivering products ranging from graphics accelerator cards to mission critical servers. Katy holds a bachelorโs degree in electrical engineering and an MBA.
Charu works in product marketing for the Enterprise Computing Platform Group at NVIDIA. He has over 20 years of experience in marketing, customer education and pre-sales of technology products and services. Charu has worked in diverse areas, such as cloud computing, hyperconverged infrastructure and IT security. As a technology marketing leader at VMware, he helped launch numerous offerings that collectively grew into multibillion-dollar businesses. Previously, he worked at Sun Microsystems, where he architected distributed resource management and HPC infrastructure software solutions. Charu has a Ph.D. in chemical engineering and is the author of several patents.
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1 Gartner - 2019 CIO Survey: CIOs Have Awoken to the Importance of AI
2 IDC, Market Analysis Perspective, Worldwide Artificial Intelligence Software, 2020, Sep 2020
3 Sumit Pal, Sr Director Analyst, Gartner, โDon't Stumble at the Last Mile: Leveraging MLOps and DataOps to Operationalize ML and AI
4 Gartner ID G00430091 October 2019
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Our team of HPE and other technology experts shares insights about relevant topics related to artificial intelligence, data analytics, IoT, and telco.