Three Keys to Successful AI Deployments

By Jeff Karmiol, IBM Product Marketing

July 27, 2018

As organizations begin to explore how Artificial Intelligence (AI) can transform their operations and improve customer experiences, the questions and concerns about how to successfully deploy emerging AI technologies can be considerable. To provide insights based on real-world AI initiatives, IBM worked with IDC to create the IDC Technology Spotlight “Accelerate and Operationalize AI Deployments Using AI-Optimized Infrastructure.”  Here are excerpts of that report.

“AI has grabbed the center stage of business intelligence, despite having been around for decades, due to the growing pervasiveness of data, the scalability of cloud computing, the availability of AI accelerators, and the sophistication of the AI Machine Learning (ML) and Deep Learning (DL) algorithms. IDC predicts that by 2019, 40% of digital transformation initiatives will use AI services; by 2021, 75% of commercial enterprise apps will use AI, over 90% of consumers will interact with customer support bots, and over 50% of new industrial robots will leverage AI.

While the power and promise of AI is exciting, deploying AI models and workloads is not easy. Despite all the buzz, most organizations are struggling through proof of concepts (POCs) and only a few have made it to full production.

AI Model and Workload Deployments: Challenges and Needs

In January 2018, IDC surveyed 405 IT and data professionals in the U.S. and Canada who had successfully completed an AI project, had budget control or influence, and were responsible for evaluating or architecting a platform to run AI workloads. The survey sought to determine how organizations use and manage AI-enabled technologies and to identify the infrastructure used for running cognitive/ML/AI workloads, the deployment location of the technology, and the associated challenges and needs. As shown in Figure 1, survey respondents identified their key AI deployment challenges as dealing with massive data volumes and associated quality and management issues.

Poor data quality has a direct correlation to biased and inaccurate model buildout. Ensuring data quality with large volumes of dynamic, diverse, and distributed data sets is a difficult task as it is hard for the developers to know, predict, and code for all the
appropriate checks and validations. View the full IDC report here.

Decision Criteria for AI Solutions

When respondents were asked by IDC for their top decision criteria in choosing an AI solution, they identified security, cost effectiveness, and operationalization (building, tuning, optimizing, training, deployment, and inferencing) of data models/intelligence, as shown in Figure 2.

Keys to Successful AI Deployments

With all these factors in mind, the essential keys to successful AI deployments are:

Data Scientist Productivity

Building, testing, optimizing, training, inferencing, and maintaining the accuracy of models is integral to AI workflow. The selection and installation of the open source frameworks and the initiation of the modeling processes can be a cumbersome affair, and it may take weeks or months to get things working. Building and optimizing models can require manually testing thousands of combinations of hyperparameters.

Training models may take weeks or months to complete in some use cases: for example, a healthcare organization took a year to build and train a medical model to detect an early-stage cancer.

Tools to automate manual model building, optimizing and training help data scientists to increase model accuracy in less time.

Optimized Infrastructure and Efficient Data Management

Both training and inferencing are compute-intensive and require high performance for fast execution. AI and DL require a new class of accelerated infrastructure primarily based on GPUs. For the linear math computations needed for training neural network models, a single system configured with GPUs is significantly more powerful than a cluster of non- accelerated systems.

Enterprise Readiness

When bringing emerging technologies and frameworks into an enterprise setting, it is critical to ensure enterprise readiness – security, reliability, support, and other criteria, as noted in Figure 2. Most of the AI/ML/DL frameworks, tool kits, and applications available do not implement security, relegating their use to disconnected experiments and lab implementations. An additional challenge in DIY-built systems is the difficulty in getting enterprise-grade support from multiple vendors.


To help organizations accelerate AI-driven business outcomes and overcome deployment obstacles, IDC offers the following guidance:

  • Focus on the business outcomes, keep the project timeline well defined, and prioritize projects with immediate revenue and cost impact.
  • Seek out software tools to simplify and automate data preparation and accelerate the iterative building, training, and deployment of AI models to drive improved business outcomes.
  • Look for dynamically adaptable, simple, flexible, secure, cost-efficient, and elastic infrastructure that can support high capacity along with high throughput and low latency for high performance training and inferencing experience.
  • Embrace intelligent infrastructure, leverage it for predictive analytics and valuable insights, then slowly phase in task automation once the trustworthiness and quality of data is established.”

View the full IDC report here.

Learn more about the IBM AI Infrastructure Reference Architecture that helps reduce the complexity of AI deployments, improve data science productivity and efficiency, and accelerate adoption of AI.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Nvidia Debuts Turing Architecture, Focusing on Real-Time Ray Tracing

August 16, 2018

From the SIGGRAPH professional graphics conference in Vancouver this week, Nvidia CEO Jensen Huang unveiled Turing, the company's next-gen GPU platform that introduces new RT Cores to accelerate ray tracing and new Tenso Read more…

By Tiffany Trader

HPC Coding: The Power of L(o)osing Control

August 16, 2018

Exascale roadmaps, exascale projects and exascale lobbyists ask, on-again-off-again, for a fundamental rewrite of major code building blocks. Otherwise, so they claim, codes will not scale up. Naturally, some exascale pr Read more…

By Tobias Weinzierl

STAQ(ing) the Quantum Computing Deck

August 16, 2018

Quantum computers – at least for now – remain noisy. That’s another way of saying unreliable and in diverse ways that often depend on the specific quantum technology used. One idea is to mitigate noisiness and perh Read more…

By John Russell

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

Super Problem Solving

You might think that tackling the world’s toughest problems is a job only for superheroes, but at special places such as the Oak Ridge National Laboratory, supercomputers are the real heroes. Read more…

NREL ‘Eagle’ Supercomputer to Advance Energy Tech R&D

August 14, 2018

The U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) has contracted with Hewlett Packard Enterprise (HPE) for a new 8-petaflops (peak) supercomputer that will be used to advance early-stage R&a Read more…

By Tiffany Trader

STAQ(ing) the Quantum Computing Deck

August 16, 2018

Quantum computers – at least for now – remain noisy. That’s another way of saying unreliable and in diverse ways that often depend on the specific quantum Read more…

By John Russell

NREL ‘Eagle’ Supercomputer to Advance Energy Tech R&D

August 14, 2018

The U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) has contracted with Hewlett Packard Enterprise (HPE) for a new 8-petaflops (peak Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

SLATE Update: Making Math Libraries Exascale-ready

August 9, 2018

Practically-speaking, achieving exascale computing requires enabling HPC software to effectively use accelerators – mostly GPUs at present – and that remain Read more…

By John Russell

Summertime in Washington: Some Unexpected Advanced Computing News

August 8, 2018

Summertime in Washington DC is known for its heat and humidity. That is why most people get away to either the mountains or the seashore and things slow down. H Read more…

By Alex R. Larzelere

NSF Invests $15 Million in Quantum STAQ

August 7, 2018

Quantum computing development is in full ascent as global backers aim to transcend the limitations of classical computing by leveraging the magical-seeming prop Read more…

By Tiffany Trader

By the Numbers: Cray Would Like Exascale to Be the Icing on the Cake

August 1, 2018

On its earnings call held for investors yesterday, Cray gave an accounting for its latest quarterly financials, offered future guidance and provided an update o Read more…

By Tiffany Trader

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17


AMD @ SC17


ASRock Rack @ SC17

ASRock Rack



DDN Storage @ SC17

DDN Storage

Huawei @ SC17


IBM @ SC17


IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17


Lenovo @ SC17


Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17


Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17


Tyan @ SC17


Univa @ SC17


  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This