Overcoming Challenges When Deploying AI Infrastructure

By Cloud and Data Center Transformation
11/10/2021

Much like television, computers, and the internet, Artificial Intelligence (AI) — when implemented properly — is radically transforming the way society and businesses operate.

Done right, AI allows businesses to generate deep analytical insights from big datasets, automate sophisticated time-consuming processes, and employ technology to interact with the environment in a humanlike way. Leveraging AI, businesses are able to excel in several key ways:

  • Providing highly tailored services and solutions for customers
  • Gaining deeper insights about customer purchasing patterns
  • Remaining competitive with other industry players
  • Dramatically reducing the cost of operations
  • Accelerating time to market

Businesses that choose to embark on their AI journey now will be the victors in tomorrow’s vastly different business market.

The journey to incorporating AI

Although the AI market value is expected to reach $360 billion by 2028 , only 12.1% of firms reported deploying AI capabilities across their environment in 2021 . With so much interest in AI, what’s causing the lack of widespread implementation? It comes down to a lack of AI expertise and support — paired with the inability to test and validate AI solutions and technologies.

Top AI challenges

Determining the right dataset, maintaining data security, and maintaining application/service congruency are all part of the process of embedding AI into an existing technology stack. When teams don’t have the resources or expertise to manage these processes and incorporate AI in an effective way, the investment may fall flat — or even contribute to new concerns. Businesses often face major challenges in several critical areas when they reach the implementation portion of their AI journey:

Expertise

  • Having access to AI expertise and tools
  • Maximizing researcher efficiency

Data

  • Ensuring sound model management, traceability, and data integrity
  • Managing data pipeline
  • Misaligned discriminatory models

Technology

  • Building an optimized AI platform
  • Performance tuning and management
  • Moving from Proof of Concept (PoC) into production

Infrastructure

  • AI environment configuration and troubleshooting
  • Optimizing compute, network, and storage
  • Support for multivendor technology stack

How to make the transition to AI more intentional

One powerful way to ease the anxiety around AI adoption is by exploring AI solutions in a lab setting. At Insight, we have our Innovation Hubs — which allow businesses to test, develop, and validate future-thinking AI solutions before ever bringing a solution into their environment. This greatly accelerates time to value for AI applications and allows businesses to avoid some of the pitfalls around AI implementation.

Today, businesses are test-driving some of the world’s fastest infrastructure at our AI Innovation Hubs, including but not limited to:

  • NVIDIA DGX A100 and DGX-2
  • NVIDIA T4
  • NetApp ONTAP AI
  • Cisco UCS C480 ML M5
  • Mellanox InfiniBand EDR and 100Gb/s Ethernet

The ability to test and validate AI technologies before committing to infrastructure can help organizations visualize new approaches to solving business problems and deliver hands-on insight into the cutting-edge technologies that can redefine operations. Customized analytics and easily accessible reporting tools, such as dashboards and scorecards, give a business access to real-time results, allowing organizational leaders to spot opportunities for growth and make informed decisions.

Some specific use cases you can explore in the Innovation Hubs include:

  • Kicking the tires on GPU-accelerated tools for managing your data pipeline and machine learning workflows, such as Iguazio, converge.io, Run:AI, and Kubeflow.
  • Testing your own JupyterHub developer environments accelerated by GPUs.
  • Exploring the NVIDIA NGC catalog of GPU-optimized software for DL/ML.
  • Visualizing the largest datasets with OmniSci.

Moving forward

When you’re ready to explore the benefits of AI, Insight’s AI Innovation Hubs are a powerful — and proven — starting point. Decades of experience providing technology support for businesses means you get the technical expertise and rich AI technology partnerships to help you find the best solution for your business.

You can learn more about our data and AI services   here.



Sources:

1 Fortune Business Insights. (September 2021). Artificial Intelligence (AI) Market Size, Share & COVID-19 Impact Analysis, Regional Forecast, 2021-2028.

2 NewVantage Partners. (2021). Big Data and AI Executive Survey 2021.