Big Data

Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) November 21, 2017

Artificial Intelligence, Machine Learning and Deep Learning (AI | ML | DL) are at the heart of digital transformation by enabling organizations to exploit their growing wealth of big data to optimize key business and operational use cases.

  • AI (Artificial Intelligence) is the theory and development of computer systems able to perform tasks normally requiring human intelligence (e.g. visual perception, speech recognition, translation between languages, etc.).
  • ML (Machine Learning) is a sub-field of AI that provides systems the ability to learn and improve by itself from experience without being explicitly programmed.
  • DL (Deep Learning) is a type of ML built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network.” A DL framework is SW that accelerates the development and deployment of these models.

See “Artificial Intelligence is not Fake Intelligence” for more details on AI | ML | DL.

And the business ramifications are staggering! (see Figure 1)

Figure 1: McKinsey “What’s Now and Next in Analytics, AI and Automation

 

And Senior Executives seem to have gotten the word. BusinessWeek (October 23, 2017) reported a dramatic increase in mentions of “artificial intelligence” during 363 third quarter earnings calls (see Figure 2).

Figure 2: Executives Mentioning “Artificial Intelligence” During Earnings Calls

 

To help our clients exploit the business and operational benefits of AI | ML | DL, Dell EMC has created “Ready Bundles” that are designed to simplify the configuration, deployment and management of AI | ML | DL solutions. Each bundle includes integrated servers, storage, networking as well as DL and ML frameworks (such as TensorFlow, Caffe, Neon, Intel BigDL, Intel Nervana Deep Learning Studio, Intel Math Kernel Library-Deep Neural Networks, and Intel Machine Learning Scaling Library) for optimized ML or deep learning.

Driving AI | ML | DL Democratization

Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” History provides democratization lessons from the Industrial and Information Revolutions. Both of these moments in history were driven by the standardization of parts, tools, architectures, interfaces, designs and trainings that allowed for the creation of common platforms. Instead of being dependent upon a “high priesthood” of specialists to assemble your guns or cars or computer systems, organizations of all sizes where able to leverage common platforms to build their own sources of customer, business and financial differentiation.

AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure. The AI | ML | DL market needs to go through a similar “standardization” process in order to create AI | ML | DL platforms that enable organizations of all sizes to build their own sources of customer, business and financial differentiation.

To help accelerate AI | ML | DL democratization, Dell EMC has created Machine Learning and Deep Learning Ready Bundles. These pre-packaged Ready Bundles de-risk and simplify AI | ML | DL projects and accelerate time-to-value by pre-integrating the necessary hardware and software (follow this link for more information on the Dell EMC Machine Learning Ready Bundles with Hadoop).

No longer is a siloed knowledge group of specialists required to stand up your AI | ML | DL environments. Instead, organizations can focus their valuable data engineering and data science resources on creating new sources of customer, business and operational value.

Monetizing Machine Learning with Dell EMC Consulting

Across every industry, organizations are moving aggressively to adopt AI | ML | DL tools and frameworks to help them become more effective in leveraging data and analytics to power their key business and operational use cases (see Figure 3).

Figure 3: AI | ML | DL Use Cases Across Industries

 

The business opportunities are plentiful. So the real challenge isn’t identifying opportunities to exploit ML for business and operational advantage, the real challenges are:

  • Identifying where and how to start integrating AI | ML | DL into business models by envisioning, identifying, validating and prioritizing the potential use cases
  • Building an elastic data platform (data repository or data lake) that enables the organization to capture, enhance, protect and share the organization’s key data and analytics digital assets.

Dell EMC Services exist to help customers bridge the gap across the data science teams, IT teams, and lines of business. Working together allows us to take the journey with you from deployment to use case development to full production.  Below are two examples of where Dell EMC has helped clients to integrate AI | ML | DL into their key business and operational processes.

Use Case #1:  Bladder Cancer Identification Using Medical Image Recognition

Image recognition of the human body is expected to improve drastically to help doctors with better and more accurate medical diagnostics. ML applied to image recognition of organs, even in the presence of disease, can minimize the possibility of medical errors and speed up disease diagnosis. This is important in many cases because a delay in diagnosis means delays in treatment. Due to the promise of these methods, medical imaging technologies will have a key role in the future of medical diagnostics and therapeutics in the very near future.

For this engagement, we used Magnetic Resonance Images (MRI) from the Cancer Imaging Archives to identify bladder cancer on patients using unsupervised and supervised ML techniques. The algorithms identified significant differences between the images and enabled physicians to see what features can be relevant for bladder cancer detection.  ML can use techniques to reduce the noise of the images and to deliver better outcomes (see Figure 4).

Figure 4: Leveraging Machine Learning to Accelerate Bladder Cancer Detection

 

The precision of the ML algorithms will increase the accuracy of the results delivered. The benefits delivered across the globe will continue to improve as more image data becomes available. Additionally, more ML models will be trained and the effectiveness of those models will be continuously refined.

It is reasonable to say that Computer Aided Tumor diagnosis using AI | ML | DL techniques will deliver important benefits to society. It will permit a reduction in the costs of healthcare and reduce the time-to-treatment while driving more effective outcomes (see Figure 5).

Figure 5: AI | ML | DL Augments Human Decision-making in Healthcare

 

Note:  This use case recently won the 2017 award as “Best of Applied Data Analytics” from Dell EMC’s Proven Professional Knowledge Sharing program.

Use Case #2: Crop Disease Identification

Human society needs to increase food production by an estimated 70% by 2050 to feed an expected population size that is predicted to be over 9 billion people.  Currently, infectious diseases reduce the potential crop yield by an average of 40% with many farmers in the developing world experiencing yield losses as high as 100%[1].

The situation is particularly dire for the 500 million smallholder farmers around the globe, whose livelihoods depend on their crops doing well. In Africa alone, 80% of the agricultural output comes from smallholder farmers.

The widespread distribution of smartphones among crop growers around the world, with an expected 5 billion smartphones by 2020, offers the potential of turning the smartphone into a valuable tool for diverse communities growing food.  One potential application is the development of mobile disease diagnostics through Deep Learning and crowdsourcing.

The results of the engagement were very impressive in scoring different types of crops and their risk to unhealthy situations (see Figure 6).

Figure 6: Crop Disease Identification and Scoring

The Dell EMC Ready Solutions + Dell EMC Consulting = Intelligent Enterprise

Dell EMC Consulting provides a full portfolio of Services designed to help our clients to accelerate AI | ML | DL adoption and monetize their data assets (see Figure 7).

Figure 7: Dell EMC Consulting Tying Together the AI | ML | DL Use Cases

 

The end goal for any organization is to master the use of AI | ML | DL to derive and drive customer, product, and operational value across the entire organization; to create the “Intelligent Enterprise” that has the ability to continuously learn and adapt to changing business, environmental, competitive and economic conditions (see Figure 8).

Figure 8: Creating “Intelligent Enterprises”

 

The future is now, and Dell EMC has joined the AI | ML | DL Ready Bundles with Dell EMC Consulting to accelerate the customer journey to the “Intelligent Enterprise.” For additional information, please visit dellemc.com/services.

Sources:

Figure 1: McKinsey “What’s Now and Next in Analytics, AI and Automation”

[1] “An open access repository of images on plant health to enable the development of mobile disease diagnostics.” 

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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