30 Days to AI Value: Development Best Practices from Intel and Aible

Speed up your time-to-value from AI by utilizing the power of Aible on top of industry-leading Intel® AI technologies and Intel® processors

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Artificial Intelligence is one of the most promising technologies in the world, but the field is plagued by high failure rates and necessarily long development cycles to move from pilot to production. This article examines AI in the enterprise space: challenges faced in AI implementation, benchmarked solutions as generated from the Intel and Aible “30-day to value” program, and what this means for business owners looking to invest in AI technology. We will also detail how Intel AI software and hardware technologies are central to Aible's approach in delivering impact.

Challenges with AI

According to Gartner, 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from prototypes to production. The deeper issue however, as uncovered by MIT-BCG, is that “a mere 10% of organizations achieve significant financial benefits with AI”. Even when AI projects are found to be successful, the technology has the potential to be unsuccessful in unexpected ways and produce unexpected results. Perhaps more significantly, the same corporations that claim to use AI are also using human biases to shape the outcomes of their algorithms; it is creating the illusion that AI makes decisions without bias when in reality, humans are still driving the decisions.

Three challenges faced by Data Scientists and AI ML Developers as per Aible customer case studies are:

  1. No one has perfectly clean data
  2. Business user role is integral
  3. AI needs to be tied to business objectives and need

AI in 30 Days

The Aible Impact

Reducing the risk and time-to-value of AI projects is imperative for the success of this ecosystem, and Aible is one of such promising startups pledged to creating value from AI within 30 days. The company is working with enterprise AI companies to transform AI's "art of possibilities" into practical applications for business—it's an example of how AI is already creating value for companies and it will only become more important in the years ahead. These are ambitious goals, but they are made possible when organizations partner with companies that can deliver on such promises.

Aible is part of the Intel® Disruptor Initiative, a global program that identifies and works with the most exciting companies building on artificial intelligence. The program brings together the world’s best AI talent with the most promising start-ups to identify and support the most promising companies building on artificial intelligence today. The goal of the Initiative is to shift the conversation from, “Will we implement AI in our organization?” to “How fast can we get value from AI?”.

Aible answers the latter by having a portfolio of products that targets the end-to-end AutoML lifecycle. From gathering requirements for specific business use cases and optimizing models via data enhancement and tuning to making available actionable insights, Aible focuses on delivering rapid business impact by automating this lifecycle. Working with Intel, key aspects of this multi-step process have been identified and optimized and have empirically Impacted how models are optimized for performance and adopted by the industry.

The Solution

Gains of Serverless Approaches

Aible takes advantage of a serverless approach to improve workload performance and further optimize their applications. This avoids the challenges with server-oriented architectures where upwards of 70% of time and costs are tied up in infrastructure overhead which isn't improved by the performance of the processor. This includes overheads for cluster scale-out, VM launch, establishing network connections, provisioning control planes, copying data, and other latencies associated with managing the operation and costs of server infrastructure.

With Aible’s serverless infrastructure, these unrelated activities and costs are mostly eliminated, and infrastructure spend goes directly towards useful compute.

Figure 1. Server & Serverless infrastructure comparison.

Aible and Intel benchmarked the overall impact on three key areas: Cost per job, Total cost per Ownership (TCO), and Elapsed training time.

All are benchmarked on two architecture environments: a serverless architecture based on AWS Lambda and AWS Server-oriented architecture based on Kubernetes.

The study demonstrated a better experience on serverless computing compared to traditional server architectures with comparable older generation Intel processors due to limitations on the AWS Lambda side.

When deployed to serverless functions the application was:

  • 2-3X more cost effective
  • 3-4X lower Total Cost of Ownership (TCO), and
  • 2-3X faster than on server architecture

Figure 2. Server vs. Serverless comparison on Cost, Time & TCO.

Given the lack of newer Icelake-based AWS Lambda, to understand the benefits of the newer Icelake architecture we studied the performance on the server-oriented architecture, specifically m6i vs. m4 instances on AWS.

We found model training to be more than twice as fast on the latest 3rd Generation Intel® Xeon® Scalable Processors over Intel® Xeon® E5 v4 processors.

*Dataset used: 250k and 500k row samples from Kaggle Bank loan default. Includes time to feature encodem model train/ test on TF 2.7, generate model metrics and model driver analysis.

Figure 3. Processor comparison: Intel Xeon E5 v4- Broadwell Vs. 3rd Gen Xeon Scalable processor (Ice Lake).

An important thing to note about Aible's benchmarks is that they are not simply based on running algorithms on a set number of data points. They also factor in things like hardware requirements, networking infrastructure, and other environmental conditions that can affect performance. This makes their results more reliable than those from other sources which may only test specific scenarios.

Analysis & Benefits of Intel® Xeon® Processors and Intel® AI Analytics Tools:

Aible was built ground up with a serverless-first mindset and from standard libraries and open source frameworks. For example, Aible was using stock ML packages like TensorFlow* 1.15 with graph execution and not all Intel optimizations were part of this framework.

Working with Intel, Aible team migrated the solution to 3rd Generation Intel Xeon Scalable Processors and  Intel-optimized ML packages with oneAPI support.

The Aible solution leverages Intel® oneAPI tools and is benefiting with faster performance and lower TCO by using the 3rd Generation Intel Xeon Scalable Processors and Intel-optimized versions of TensorFlow, NumPy, and SciPy.

Highlighted Improvements

TensorFlow 2.7+ is Intel optimized: Aible benefitted from Intel® AVX-512 and showed the following performance gains:

  • 2X for classification or regression models
  • up to 20X for business use cases leveraging transfer learning incorporating anguage (transformer) or Image (CNN) models

Intel-optimized versions of NumPy and SciPy: Aible showed the following performance gains:

  • 2X across the board speed up – without any changes to Aible code
  • 15-20X speedup for transcendental functions e.g. np.exp, np.log
  • 2X+ speedup of scipy.special.logsumexp and scipy.stats.norm

The overall performance gains showed that Aible, when run on 3rd Generation Intel® Xeon® Scalable Processors, delivers results in half the time over older generation Intel® Xeon® E5 v4 processors. Aible being serverless first, if serverless configuration were to use newer generation processors – companies like Aible would benefit from higher performance, which in turn would translate to their customers benefitting from gaining faster insights in less amount of time.

The Result

Organizations are enabled to make quicker, better strategic, and tactical decisions that deliver business value quickly.

Customer Case Studies

The Aible MLOPS solution on Intel® Architecture has offered 25 organizations access to the AI, enabling them to solve their toughest business problems ranging from organizing the company holiday party to optimizing the company's IT strategy. Each of these organizations has worked closely with a dedicated team of Aible and Intel experts to see the solution in action. In many cases, these engagements have been completed in less than 30 days.

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The Enterprise AI Solution of Choice

Today’s enterprise IT infrastructure leaders face significant challenges in building a foundation that is designed to help teams drive value from AI initiatives in the data center. Aible has been helping business teams across key industries deliver measurable business impact from AI within days by using Intel Xeon Scalable processors with Intel-optimized AI software. The reduced risk and time-to-value delivered by the Aible enterprise AI solutions, powered by Intel, are central to the vision of an AI Everywhere future.

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About Aible

Aible is an end-to-end and automated AI solution that eliminates the need for organizations to manage the AI infrastructure and data. With Aible, organizations can focus on their business challenges and maximize the impact of AI. This is validated by long list of case studies published here, that details the challenges and timeline to success. Aible provides enterprise AI solutions, transforming how companies make strategic decisions, act optimally, react to changes, and align across the organization using AI as an enabler for collaboration at scale. It offers tools for conducting scenario analysis and assumption testing, advises on the predictive model and resourcing, creates datasets for model retraining, enables end-users to provide direct feedback, monitors business outcomes, and more. Discover Aible >

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