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The Four Paths to AI Success

MaryT_Intel
Community Manager
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Find the right approach to artificial intelligence (AI) for your organization

Perceptions of artificial intelligence (AI) in the enterprise are evolving. No longer seen as reserved for the most technologically advanced organizations, AI is now featured in many businesses’ toolboxes, with IDC predicting that 75 percent of enterprise applications will integrate AI by 2021[1].

Each organization is unique in terms of AI-readiness. The nature of your needs, the maturity of any existing data sciences capabilities, and the level of expertise you have in-house are just some of the factors that will impact your AI strategy. Whatever your starting point, work closely with Intel as your trusted advisor to build and develop your AI strategy. In this article we’ll explore some of the paths you might take.

Four paths to AI success

Path 1: Collaboration

If your company is completely new to AI and still developing its analytics capabilities in house, a good way to start would be to work with one of Intel’s partners who has the experience, skills and resources to help set you up for success. This helps take the guesswork out of your first AI projects, reduce risk and decrease time-to-market.

This approach can also work well for companies with well-developed in-house data science and AI expertise if they need to accelerate the time-to-market of their AI solutions, or complement their in-house resources with external experts.

You can find the right partner— independent software vendors, system integrators, or OEMs—through the Intel® AI Builders program, which brings together hundreds of solution providers across industries, geographies, AI workloads and specialties. They offer ready-made AI solutions for a broad range of business challenges, such as automating contact center interactions with customers, or forecasting market performance to optimize investment decisions.

Path 2: Cloud-based tools

Many organizations today have adopted a cloud-first strategy. In this case, you may consider using an Intel-optimized cloud-based AI instance that will offer scalability and flexibility to adjust as your needs evolve, while minimizing the amount of work to be done in-house. You may choose to combine this approach with Collaboration (Path 1), using a specialist to help with deployment.

Intel has worked with many leading cloud service providers (CSPs) to develop machine and deep learning instances, optimized for Intel® architecture and pre-imaged with optimized versions of industry-leading AI software, including:

  • AWS: Optimized EC2 instances for machine learning (including deep learning) or compute-intensive applications
  • Microsoft Azure: Intel-optimized Data Science Virtual Machine (DSVM)
  • Google Cloud Platform: TensorFlow optimizations for Intel platforms and the latest Intel machine and deep learning instances
  • Baidu: Baidu’s open source deep learning platform, PaddlePaddle, runs on Intel Xeon Scalable processors and is optimized at the computing, memory, architecture and communication levels to achieve improved model deployment performance.

Using cloud-based tools for AI is a common element of hybrid and multi-cloud strategies. These approaches generally include a mixture of Path 2, and Path 3.

Path 3: On-Premise Platform

When you’re clear on what you need from your AI solution – including fast time-to-market and cost effectiveness – you may choose to work with an original equipment manufacturer (OEM) or an original device manufacturer (ODM) to deploy an AI platform in the datacenter or your private cloud that best suits your needs.

Intel has worked with leading OEMs to develop AI solutions that will help you get up and running quickly. These include:

  • Dell EMC Ready Solutions for AI, which offer pre-designed and pre-validated solutions ideal for machine learning and deep learning.
  • HPE offers AI solutions focusing on video surveillance, fraud detection, prescriptive maintenance, autonomous driving, speech to text, smart cities and more.
  •  Inspur has training and inference solutions based on Intel® Xeon® processors and has recently released inference accelerators based on Intel Xeon processors and Intel® FPGAs.
  • Lenovo intelligent Computing Orchestrator (LiCO), based on Intel technologies, simplifies enterprise AI deployment with an intuitive interface for managing AI workloads.

Path 4: Build-your-own

If you already have data science experts in house, who want to extend their infrastructure to highly optimized physical servers with capacity to support AI workloads, then you could consider building your own AI environment. This approach is generally best suited to organizations that have relatively mature infrastructure operations and in-house data science staff with specific AI application needs. That said, this doesn’t have to mean significant investment of time and budget into building a new infrastructure. You can run most AI workloads on your existing Intel® technology-based platform.

For some of the most common AI workloads, you can use turnkey validated solution architectures—Intel® Select Solutions–that are also available pre-built through system vendors—to help you deploy your own solution and accelerate time to results.

Identifying the right path to AI for your organization is an important first step, and we’re here to help. If you’re ready to embark on your journey to AI, let us know and we’ll help put you in touch with the team that will help you get there.

 [1] IDC Market Forecast, Worldwide Storage for Cognitive/AI Workloads Forecast, 2018–2022

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About the Author
Mary is the Community Manager for this site. She likes to bike, and do college and career coaching for high school students in her spare time.