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.

Conclusion

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.

Return to Solution Channel Homepage

IBM Resources

Follow @IBMSystems

IBM Systems on Facebook

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!

CFD on ORNL’s Titan Simulates Cleaner, Low-MPG ‘Opposed Piston’ Engine

December 13, 2018

Pinnacle Engines is out to substantially improve vehicle gasoline efficiency and cut greenhouse gas emissions with a new motor based on an “opposed piston” design that the company hopes will be widely adopted while t Read more…

By Doug Black

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC) is procuring from Atos in two phases over the next year-an Read more…

By Tiffany Trader

Nvidia Leads Alpha MLPerf Benchmarking Round

December 12, 2018

Seven months after the launch of its AI benchmarking suite, the MLPerf consortium is releasing the first round of results based on submissions from Nvidia, Google and Intel. Of the seven benchmarks encompassed in version Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

AI Can Be Scary. But Choosing the Wrong Partners Can Be Mortifying!

As you continue to dive deeper into AI, you will discover it is more than just deep learning. AI is an extremely complex set of machine learning, deep learning, reinforcement, and analytics algorithms with varying compute, storage, memory, and communications needs. Read more…

IBM Accelerated Insights

4 Ways AI Analytics Projects Fail — and How to Succeed

“How do I de-risk my AI-driven analytics projects?” This is a common question for organizations ready to modernize their analytics portfolio. Here are four ways AI analytics projects fail—and how you can ensure success. Read more…

Neural Network ‘Synapse’ Technology Showcased at IEEE Meeting

December 12, 2018

There’s nice snapshot of advancing work to develop improved neural network “synapse” technologies posted yesterday on IEEE Spectrum. Lower power, ease of use, manufacturability, and performance are all key paramete Read more…

By John Russell

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Nvidia Leads Alpha MLPerf Benchmarking Round

December 12, 2018

Seven months after the launch of its AI benchmarking suite, the MLPerf consortium is releasing the first round of results based on submissions from Nvidia, Goog Read more…

By Tiffany Trader

IBM, Nvidia in AI Data Pipeline, Processing, Storage Union

December 11, 2018

IBM and Nvidia today announced a new turnkey AI solution that combines IBM Spectrum Scale scale-out file storage with Nvidia’s GPU-based DGX-1 AI server to pr Read more…

By Doug Black

Mellanox Uses Univa to Extend Silicon Design HPC Operation to Azure

December 11, 2018

Call it a corollary to Murphy’s Law: When a system is most in demand, when end users are most dependent on the system performing as required, when it’s crunch time – that’s when the system is most likely to blow up. Or make you wait in line to use it. Read more…

By Doug Black

Topology Can Help Us Find Patterns in Weather

December 6, 2018

Topology--the study of shapes--seems to be all the rage. You could even say that data has shape, and shape matters. Shapes are comfortable and familiar concepts, so it is intriguing to see that many applications are being recast to use topology. For instance, looking for weather and climate patterns. Read more…

By James Reinders

Zettascale by 2035? China Thinks So

December 6, 2018

Exascale machines (of at least a 1 exaflops peak) are anticipated to arrive by around 2020, a few years behind original predictions; and given extreme-scale performance challenges are not getting any easier, it makes sense that researchers are already looking ahead to the next big 1,000x performance goal post: zettascale computing. Read more…

By Tiffany Trader

Robust Quantum Computers Still a Decade Away, Says Nat’l Academies Report

December 5, 2018

The National Academies of Science, Engineering, and Medicine yesterday released a report – Quantum Computing: Progress and Prospects – whose optimism about Read more…

By John Russell

Revisiting the 2008 Exascale Computing Study at SC18

November 29, 2018

A report published a decade ago conveyed the results of a study aimed at determining if it were possible to achieve 1000X the computational power of the the Read more…

By Scott Gibson

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

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

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Google Releases Machine Learning “What-If” Analysis Tool

September 12, 2018

Training machine learning models has long been time-consuming process. Yesterday, Google released a “What-If Tool” for probing how data point changes affect a model’s prediction. The new tool is being launched as a new feature of the open source TensorBoard web application... Read more…

By John Russell

The Convergence of Big Data and Extreme-Scale HPC

August 31, 2018

As we are heading towards extreme-scale HPC coupled with data intensive analytics like machine learning, the necessary integration of big data and HPC is a curr Read more…

By Rob Farber

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