Machine learning platforms powered by Intel technology can help organizations transform data into business insights. These platforms provide scalability, efficiency and lower costs while reducing time to market for intelligent solutions. Intel's high-performance computing reference architectures are optimized for machine learning and include scalable hardware and software for predictive analytics. Using an Intel-based machine learning platform allows organizations to gain a competitive edge through accelerated model training and deployment.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Machine learning plays a significant role in developing these models. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks. This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions inreal time without human intervention are playing critical role in this age. All of these require models thatcan automatically analyse large complex data and deliver quick accurate results – even on a very largescale. Machine learning plays a significant role in developing these models. The applications of machinelearning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representationand classification methods for developing hardware for machine learning with the main focus on neuralnetworks. This paper also presents the requirements, design issues and optimization techniques for buildinghardware architecture of neural networks.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
The analytics market is abuzz where professionals from various disciplines and background are leveraging data in their daily activities to get maximum insights and help a business to grow.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Andmekeskuste töökindlusele ja kiirusele esitatavate nõuetega koos on kasvanud süsteemide keerukus. Uue põlvkonna konvergentsilahendused lihtsustavad arhitektuuri, vähendavad halduskoormust ja suurendavad seejuures süsteemi üldist töökindlust. Kuidas SimpliVity selle saavutab ja mida veel silmas pidada.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Machine learning plays a significant role in developing these models. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks. This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks.
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions inreal time without human intervention are playing critical role in this age. All of these require models thatcan automatically analyse large complex data and deliver quick accurate results – even on a very largescale. Machine learning plays a significant role in developing these models. The applications of machinelearning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representationand classification methods for developing hardware for machine learning with the main focus on neuralnetworks. This paper also presents the requirements, design issues and optimization techniques for buildinghardware architecture of neural networks.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
The analytics market is abuzz where professionals from various disciplines and background are leveraging data in their daily activities to get maximum insights and help a business to grow.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Andmekeskuste töökindlusele ja kiirusele esitatavate nõuetega koos on kasvanud süsteemide keerukus. Uue põlvkonna konvergentsilahendused lihtsustavad arhitektuuri, vähendavad halduskoormust ja suurendavad seejuures süsteemi üldist töökindlust. Kuidas SimpliVity selle saavutab ja mida veel silmas pidada.
Vertex Perspectives | AI Optimized Chipsets | Part IIVertex Holdings
Deep learning is both computationally and memory intensive, necessitating enhancements in processor performance. In this issue, we explore how this has led to the rise of startups adopting alternative, innovative approaches and how it is expected to pave the way for different types of AI-optimized chipsets.
AI for RoI - How to choose the right AI solution?Abhinav Singhal
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
Vertex Perspectives | AI Optimized Chipsets | Part IVVertex Holdings
In this instalment, we delve into other emerging technologies including neuromorphic chips and quantum computing systems, to examine their promise as alternative AI-optimized chipsets.
Vertex perspectives ai optimized chipsets (part i)Yanai Oron
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning.
A technical Introduction to Big Data AnalyticsPethuru Raj PhD
This presentation gives the details about the sources for big data, the value of big data, what to do with big data, the platforms, the infrastructures and the architectures for big data analytics
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
For the bi-monthly Twente Data Meetup, Jeroen Linssen gave a presentation on the lessons learned in various research projects related to smart industry, carried out in the research group Ambient Intelligence.
Data Science is a new technology, which is basically used for apply critical analysis. It utilizes the potential and scope of Hadoop. It also helps fully in R programming and machine learning implementation. It is a blend of multiple technologies like data interface, algorithm. It helps to solve an analytical problem. Data Science provides a clear understanding of work in big data, analytical tool R. Also, it provide the analyses of big data. It gives a clear idea of understanding of data, transforming the data. Also, it helps in visualizing the data, exploratory analysis, understanding of null value. It used to impute the value with the help of different rules and logic.
Learn about the Ibm research report on Understanding System and Architecture for Big Data.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
CHALLENGES FOR MANAGING COMPLEX APPLICATION PORTFOLIOS: A CASE STUDY OF SOUTH...IJMIT JOURNAL
This research explores the challenges in management and the root cause for complex application portfolios
in the public sector. It takes Australian public sector organisations with the case of South Australia Police
(SAPOL) for evaluation it being one of the significant and mission critical state government agencies. The
exploratory research surfaces some of the key challenges using interview as primary data collection
source, along with archive records, documentation, and direct observation as secondary sources. This
paper reports on the information analysed surfacing eight key issues. It highlights that the organic growth
of the technology portfolios, with mission criticality has resulted in many quick fixes which are not aligned
with long term enterprise architectural stability. Integration of different mismatched technologies, along
with the pressure from the business to always keep the lights on, does not provide the opportunity for the
portfolios to be rationalised in an ongoing way. Other issues and the areas for further study are explored
at the end.
Learn how IBM Storage and Software Defined Infrastructure help leading financial services institutions meet the challenges of:
- Engagement
- Agility
- Risk and Compliance
...and how our offerings enable the companies to maintain leadership today and in the future.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
расположен в дельте Волги на острове Аккусинский, на слиянии трёх рек — Артельная, Никитинский банк, Бакланья, в 6 км южнее села Жан-Аул Камызякского района Астраханской области, в 57 км от Астрахани.
Vertex Perspectives | AI Optimized Chipsets | Part IIVertex Holdings
Deep learning is both computationally and memory intensive, necessitating enhancements in processor performance. In this issue, we explore how this has led to the rise of startups adopting alternative, innovative approaches and how it is expected to pave the way for different types of AI-optimized chipsets.
AI for RoI - How to choose the right AI solution?Abhinav Singhal
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
Vertex Perspectives | AI Optimized Chipsets | Part IVVertex Holdings
In this instalment, we delve into other emerging technologies including neuromorphic chips and quantum computing systems, to examine their promise as alternative AI-optimized chipsets.
Vertex perspectives ai optimized chipsets (part i)Yanai Oron
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning.
A technical Introduction to Big Data AnalyticsPethuru Raj PhD
This presentation gives the details about the sources for big data, the value of big data, what to do with big data, the platforms, the infrastructures and the architectures for big data analytics
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
For the bi-monthly Twente Data Meetup, Jeroen Linssen gave a presentation on the lessons learned in various research projects related to smart industry, carried out in the research group Ambient Intelligence.
Data Science is a new technology, which is basically used for apply critical analysis. It utilizes the potential and scope of Hadoop. It also helps fully in R programming and machine learning implementation. It is a blend of multiple technologies like data interface, algorithm. It helps to solve an analytical problem. Data Science provides a clear understanding of work in big data, analytical tool R. Also, it provide the analyses of big data. It gives a clear idea of understanding of data, transforming the data. Also, it helps in visualizing the data, exploratory analysis, understanding of null value. It used to impute the value with the help of different rules and logic.
Learn about the Ibm research report on Understanding System and Architecture for Big Data.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
CHALLENGES FOR MANAGING COMPLEX APPLICATION PORTFOLIOS: A CASE STUDY OF SOUTH...IJMIT JOURNAL
This research explores the challenges in management and the root cause for complex application portfolios
in the public sector. It takes Australian public sector organisations with the case of South Australia Police
(SAPOL) for evaluation it being one of the significant and mission critical state government agencies. The
exploratory research surfaces some of the key challenges using interview as primary data collection
source, along with archive records, documentation, and direct observation as secondary sources. This
paper reports on the information analysed surfacing eight key issues. It highlights that the organic growth
of the technology portfolios, with mission criticality has resulted in many quick fixes which are not aligned
with long term enterprise architectural stability. Integration of different mismatched technologies, along
with the pressure from the business to always keep the lights on, does not provide the opportunity for the
portfolios to be rationalised in an ongoing way. Other issues and the areas for further study are explored
at the end.
Learn how IBM Storage and Software Defined Infrastructure help leading financial services institutions meet the challenges of:
- Engagement
- Agility
- Risk and Compliance
...and how our offerings enable the companies to maintain leadership today and in the future.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
расположен в дельте Волги на острове Аккусинский, на слиянии трёх рек — Артельная, Никитинский банк, Бакланья, в 6 км южнее села Жан-Аул Камызякского района Астраханской области, в 57 км от Астрахани.
Think big data, and think opportunity. That is, think beyond storing and managing data, and leverage analytics to derive more value than imaginable from your business intelligence. This white paper offers a forward thinking, collaborative approach to analyzing data and changing the way you think about business.
Digital Transformation: How to Run Best-in-Class IT Operations in a World of ...Precisely
IT leaders looking to move beyond reactive and ad hoc troubleshooting need to find the intersection of maintaining existing systems while still driving innovation - solving for the present while preparing for the future. Identifying ways to bring existing infrastructure and legacy systems into the modern world can create the business advantage you need.
View the conversation with Splunk’s Chief Technology Advocate, Andi Mann and Syncsort’s Chief Product Officer, David Hodgson where we discuss the digital transformation taking place in IT and how machine learning and AI are helping IT leaders create a more business-centric view of their world including:
• The importance of data sharing and collaboration between mainframe and distributed IT
• The value of integrating legacy data sources and existing infrastructure into the modern world
• Achieving an end to end view of IT operations and application performance with machine learning
This draft paper throws light on data center technology trends of 2016. This paper also suggest ways to enhance the competitiveness of Data Center. We have tried to carve out a strategy that can help decision makers to decide whether a technology adoption will prove beneficial for them or they will end up spending more without any significant ROI.
MongoDB World 2019: Data Digital DecouplingMongoDB
Why data decoupling? Learn how enterprises are pivoting to decouple big monolith and legacy data platform to smaller chunk and freedom to run anywhere and run multi-cloud agility for their business
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
Denis Jannot - Towards Data Science Engineering Principles - Codemotion Milan...Codemotion
Over the last half century we have developed and refined the discipline of software engineering in order to accelerate the development and deployment of applications. This has involved a general shift towards DevOps practices that align developer and business objectives and dramatically reduce time-to-delivery. With the recent rise of data science and data analytics, the time has come to apply the principles of DevOps to data science and leverage the lessons from software engineering (and its systematic and repeatable methodology) to the discipline of data science.
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...Denodo
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...Vasu S
A whitepaper of TDWI checklist, drills into the data, tools, and platform requirements for machine learning to to identify goals and areas of improvement for current project
https://www.qubole.com/resources/white-papers/tdwi-checklist-the-automation-and-optimzation-of-advanced-analytics-based-on-machine-learning
4 Advantages Artificial Intelligence Can Offer Industry 4.pptxArpitGautam20
Here are a few advantages that Ai brings to the table for Industry 4.0. These can change the way industries leverage Ai in the days to come. https://arsr.tech/4-advantages-artificial-intelligence-can-offer-industry-4-0/
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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10011111010001011010101100111110100010110101011001111101000101101010110011111010001011010101100111110100010
1001111101000101101010110011111010001011010101100111110100010110101011001111101000101101010
1001111101000101101010110011111010001011010101100111110100010110101011001111101000101101010110011111010001011
10011111010001011010101100111110100010110101011001111101000101101010110011111010001011010
100111110100010110101011001111101000101101010110011111010001011010101100111110100010110101011001111101000101101010110011111010001011010101
1001111101000101101010110011111010001011010101100111110100010110101011001111101010001011010101
10011111010001011010101100111110100010110101011001111101000101101010110011111010001011010101100
100111110100010110101011001111101000101101010110011111010001011010101100111110100010110101011001111101000101101010110011111010001011010101
10011111010001011010101100111110100010110101011001111101000101101010110011111010001011010101100110001011010101
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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