1. Authors
Nidhi Chappell
Director, Machine Learning
Datacenter Group
Herbert Cornelius
Principal HPC Solutions Architect,
Influencer Sales Group
Machine learning platforms powered by Intel® technology help transform data
into actionable business intelligence through accelerated model training, fast
scoring, and highly scalable infrastructure
Accelerate Intelligent Solutions
with a Machine Learning Platform
Executive Summary
Machine learning enables businesses and organizations to discover insights
previously hidden within their data. Whether exploring oil reserves, improving the
safety of automobiles, or mapping genomes, machine learning algorithms are at
the heart of innovation and business intelligence.
Unleashing the power of machine learning, however, requires certain ingredients:
access to large amounts of diverse data and the right skill sets, optimized data
platforms, powerful data analysis tools, and a highly scalable and flexible compute
and storage infrastructure.
Intel’s high-performance computing (HPC) reference architectures are optimized
for machine learning. Built on a hardware foundation that incudes compute,
memory, storage, and network, these platforms include an optimized, scalable
software stack for predictive analytics.
By using a machine learning platform based on Intel® architecture, businesses
can gain scalability, effectiveness, efficiency, and lower total cost of ownership
(TCO) while reducing time to market for intelligent solutions that can give them a
competitive market edge.
Solution Brief
Data Center
High-Performance Computing
Figure 1. Insights are there, but they lie buried in huge volumes of data. Machine
learning can help companies uncover those insights, which they can use to develop
innovative, intelligent solutions.
Deep Insights Provide
Competitive Edge
Machine Learning
with Smarter Algorithms
Processes overwhelming
volumes of data
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2. Solution Brief | Accelerate Intelligent Solutions with a Machine Learning Platform 2
Business Challenge:
Using Machine Learning Effectively
Businesses in every industry can gain a competitive advantage
and generate new revenue by delivering intelligent products
and services that are more personalized, efficient, and
adaptive. But CIOs are buried in data—the challenge lies
in effectively using machine learning techniques (Figure 2)
such as deep learning, computational statistics, mathematical
optimization, and artificial neural networks to build
intelligence into solutions.
Machine learning – an outgrowth of artificial intelligence –
enables researchers, data scientists, engineers, and analysts
to automate analytical model building by constructing
algorithms that can learn from and make predictions based
on data. The explosion of big data has made machine
learning an important differentiating factor across many
industries. For example, bioinformatics’ high-throughput
techniques can rapidly produce terabytes of data that
overwhelm conventional biological analysis. Ultra-scalable,
high-performance machine learning platforms, however, can
quickly process vast amounts of data.
Machine learning also has applications in the areas of
modeling web browsing behavior, spam filtering, optical
character recognition, and fraud detection, just to name a few.
However, the powerful potential of machine learning seems
out of reach for many organizations. Using machine learning
technologies effectively can be challenging. To be successful,
the following elements are necessary:
• Access to large amounts of diverse data
• Optimized data and compute platforms to manage and
process data
• Powerful data analysis software to build sophisticated
predictive models
• A highly scalable, flexible infrastructure (compute, memory
and storage, and network) on which to develop, train, and
deploy models based on machine learning
• A pool of appropriately skilled talent, such as data scientists
and solution developers, that can efficiently manage insights
from data
Machine Learning Use Cases
Span Multiple Industries
Whether an organization is developing models for disease
prevention or storm prediction, machine learning can
speed results while delivering a higher degree of accuracy.
Retailers can better predict customer purchases and reduce
customer churn by delivering targeted offers; utilities can
more accurately forecast and prevent potential outages;
and companies can better automate help desk services and
improve customer service.
For example, in Europe, more than a dozen banks have
replaced older statistical-modeling approaches with machine
learning techniques and, in some cases, experienced
10-percent increases in sales of new products, 20-percent
savings in capital expenditures, 20-percent increases in cash
collections, and 20-percent declines in churn.1
Figure 2. Various machine learning techniques pose unique challenges, but they require common elements of data, skill sets,
and the right platform components.
Machine Learning Data Processing
Curation
Identify sources and understand relationships
Variety is massive, continuously new sources
Training
Train an algorithm to build a model
Model build time is critical
Scoring
Deploy models for classification,
prediction, and recognition of new data
Requires easy distribution, sensitive
throughput, and TCO at scale
Deep Learning Technique
• Many hidden layers
• Features are learned
• Complex data
Other Techniques
Techniques include: computational statistics,
mathematical optimization, and artificial
neural networks
• Clustering, regression, and classification
using one or two hidden layers
• Features are engineered
Using Machine Learning Techniques Effectively
“Humans can typically create one or two good
models a week; machine learning can create
thousands of models a week.”2
—Thomas H. Davenport
Analytics Thought Leader
(excerpt from The Wall Street Journal)

3. Solution Brief | Accelerate Intelligent Solutions with a Machine Learning Platform 3
Solution Value: New Insights Enable
Better Business Decisions
Intel’s high-performance computing (HPC) machine
learning reference architectures offer the following
enterprise benefits:
• Shorter time to train models, with scalable multi-node
configuration for complex neural networks
• High throughput scoring on standard, energy-efficient
server-class infrastructure
• A single architecture for multiple advanced analytics
requirements
Organizations can build accurate models faster and
deploy intelligent solutions quickly, while decreasing total
cost of ownership (TCO). Intel’s portfolio of innovative
technologies – both hardware and software – provide
balance, portability, and high performance through tight,
system-level integration and modernized code.
Intel’s optimized HPC technologies lay the foundation
for a holistic machine learning platform. This platform
is built on industry-standard hardware and can be
deployed on‑premises or in public or hybrid cloud
environments. The platform can scale from small clusters
to supercomputers. Intel’s ongoing investment in HPC
technologies opens the path for new parallel, neural,
and quantum computing options.
With attractive performance and TCO provided by platforms
based on Intel’s machine learning reference architectures,
companies can optimize value from their data through
advanced analytics.
Solution Architecture: Fully Optimized
Machine Learning Environment
Intel’s machine learning reference architectures help companies
build platforms that can tap into the power of machine learning.
An Intel® architecture-optimized infrastructure serves as the
foundation for these platforms, which are ideally suited for a
broad range of machine learning workloads.
• Compute. Servers equipped with Intel® Xeon® processors
help keep costs affordable while delivering exceptional
performance, agility, reliability, and security. Intel® Xeon
Phi™ processors and coprocessors offer highly parallel
performance and can scale to over 100 software threads,
make extensive use of vectors, and efficiently use local
memory bandwidth—a benefit for the iterative nature of
machine learning workloads. Other technologies include
built-in field-programmable gate array (FPGA) modules for
augmented specific acceleration.
• Memory and storage. As model sizes increase, it is
important to keep data close to memory to reduce latency
while processing large data sets. Example technologies
include 3D XPoint™ technology, Intel® Optane™ technology,
and rugged, high-performance PCI Express*- and non-
volatile memory Express-based solid-state drives (SSDs).
• Network. Effective machine learning platforms require a
high-performance, low-latency fabric like Intel Omni-Path
Architecture to maximize memory capacity and floating-point
performance and accelerate results. Example technologies
include highly scalable Intel® Omni-Path architecture and
Intel® Ethernet Server Adapters (10 GbE and 40 GbE).
As shown in Figure 3, scalable data and analytics platforms
are layered on this infrastructure, which can then efficiently
run individual analytics applications.
fASterDeCiSionS3
Companies that use analytics are:
• 5x more likely to make “much
faster” decisions than competition
• 2x more likely to have top-quartile
financial performance
• 3x more likely to execute decisions
as intended
• 2x more likely to frequently use
data when making decisions
Figure 3. A fully optimized machine learning environment is
built on tightly integrated Intel® technologies for accelerated
insight discovery at a lower cost of ownership.
Applications
Analytics-powered vertical and
horizontal solutions
Machine
Learning
Frameworks
and Algorithms
Multi-layered, fully
optimized algorithms
• Intel® Math Kernel
Library
• Intel® Data Analytics
Acceleration Library
Performance
and Security
Silicon and
software
enhancements to
protect and
accelerate data
and analytics
Trusted Analytics Platform
Open-source platform for
collaborative data science and
analytics app development
Data
Open-source, Hadoop-centric
platform for distributed and scalable
storage and processing
Infrastructure Optimized
for Intel® Architecture
Software-defined storage, virtualized
compute, networking, and cloud
Scalable Data and Analytics Platforms

4. Solution Brief | Accelerate Intelligent Solutions with a Machine Learning Platform 4
1
McKinsey Quarterly, June 2015, “An executive’s guide to machine learning,” http://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning
2
Source: http://www.sas.com/en_us/insights/analytics/machine-learning.html
3
Bain and Company, September 11, 2013, “Big Data: The organizational challenge,” http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured
using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and
performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit
intel.com/performance.
Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and provide cost savings.
Circumstances will vary. Intel does not guarantee any costs or cost reduction.
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration.
No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document .
Copyright © 2016 Intel Corporation. All rights reserved. Intel, the Intel logo, Xeon, Xeon Phi, 3D XPoint, and Optane are trademarks of Intel Corporation in the U.S. and/or other countries.
*
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Each layer is optimized to provide extremely high
performance:
• Machine learning frameworks like Spark MLlib*, Caffe*,
Theano*, Torch*, and CNTK* can scale across distributed
systems based on Apache Spark* and MPI (Message-Passing
Interface). Through code modernization, these frameworks
are optimized to take advantage of parallelization at the
thread, data, and vector levels, as well as the innovative
memory/storage hierarchy.
• Machine learning algorithms can extract exceptional
performance from the underlying hardware platform using
mathematical building blocks from the Intel® Math Kernel
Library and the Intel® Data Analytics Acceleration Library,
which are tuned for high performance on Intel® processors.
• The performance and security of data acquisition, data
management, and data processing engines are enhanced
by open source products such as Apache Spark, Apache
Hadoop*, and the Lustre* file system as well as optimized
proprietary software products.
Intel invests in building the broadest ecosystem possible
and works closely with fellow travelers including academic
researchers, OEMs, communications service providers (CSPs),
ISVs, and system integrators. The result is machine learning
technologies customized for your solution.
Conclusion
Machine learning is becoming a business necessity; it
can help organizations quickly build models that enable
development of intelligent solutions—which in turn create
new revenue streams and differentiate those organizations
from their competitors. But whether exploring underground
oil reserves or improving the safety of automobiles,
organizations need a highly scalable, balanced and robust
infrastructure for HPC that speeds discovery and innovation
and decreases time to market.
Platforms based on Intel’s machine learning reference
architectures and HPC solutions help enable organizations to
fully realize the benefits of machine learning, while increasing
performance and decreasing TCO. Intel is continuing to invest
in HPC technology to help organizations meet the machine
learning needs of tomorrow, driven by the explosion of data
and the Internet of Things.
Find the solution that’s right for your organization.
Contact your Intel representative, register at Intel IT
Center or visit intel.com/machinelearning.
Solutions Proven By Your Peers
Intel Solution Architects are technology experts who
work with the world’s largest and most successful
companies to design business solutions that solve
pressing business challenges. These solutions are based
on real-world experience gathered from customers
who have successfully tested, piloted, and/or deployed
these solutions in specific business use cases. Solution
architects and technology experts for this solution brief
are listed on the front cover.