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Machine Learning

Machine learning and the larger world of artificial intelligence (AI) are no longer the stuff of science fiction. They’re here – and many businesses are already taking advantage. As a new breed of software that is able to learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. And now we’ve made it easier to unlock its potential with embedded machine learning capabilities and services easily accessible through the cloud.

Explore SAP Leonardo Machine Learning

Introduction to machine learning

Artificial intelligence, machine learning, and deep learning are often used interchangeably, but they’re not the same. In a nutshell, AI is the broader concept of machines that can act intelligently. Machine learning and deep learning are sub-sets of AI based on the idea that given access to large volumes of data, machines can learn for themselves. Read on for more about deep learning vs. machine learning, and other important terms.

What is machine learning?

The simplest machine learning definition? The practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. So rather than programming software to accomplish a specific task, the machine uses Big Data and sophisticated algorithms to learn how to perform the task itself. Machine learning allows applications to “think” and independently make a determination or prediction – going beyond what predictive analytics and Big Data analytics can do, and often beyond what humans can do. A popular consumer example of machine learning is a recommendation engine in an online retail environment.

What is deep learning?

Deep learning, sometimes known as cognitive computing, is a form of advanced machine learning. It uses multi-layered (aka deep) neural networks to simulate human thought processes. These networks are made up of small computing nodes that mimic the synapses of the human brain. Using input data sets and sophisticated algorithms, machines can help solve complex, non-linear problems. Deep learning is responsible for breakthroughs such as speech and image recognition and natural language processing. Some popular deep learning examples include:
  • Facial recognition software
  • Self-driving cars
  • Smart home automation devices

Supervised vs unsupervised learning

There are three main ways that machines can “learn”:
  • Supervised learning – in this approach humans label the inputs and outputs and then the model figures out the rules for connecting the two.
  • Semi-supervised (or reinforcement) learning – the machine is
    rewarded or penalized for actions it takes through trial and error, and the algorithm adjusts accordingly.
  • Unsupervised learning – algorithms are left to discover patterns in the data (which is sometimes clustered) on their own.
Regardless of the type of training used, the machine is able to learn from the data on its own, absorbing new behaviors and functions over time. The result is a model which can be used to predict outcomes based on data, and which is regularly retrained for accuracy.

Why is machine learning advancing so rapidly?

Machine learning is not a new concept, but it has recently gained fresh momentum. Why? Processing power and storage are now more affordable than ever, and the explosion of Big Data from various sources – such as text, images, and IoT devices – is making it easier for machines to “train” and learn.

AI and machine learning in action

Benefits of machine learning

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Faster decision making

Machine learning can automate and prioritize routine decision making processes – so you can achieve best outcomes sooner. For example, when coupled with the Internet of Things, it can help you decide what to fix first in your manufacturing plant.

Adaptability

Your data is constantly being updated, which means your machine learning models will be too – much faster than humans can currently develop them. This lets you quickly discover and process new insights to adapt to rapidly changing business environments.

Innovation and growth

An “algorithmic business” uses advanced algorithms to drive process automation and improved decision making. Making the shift can accelerate overall knowledge harvesting and pave the way for innovative business models, products, and services.
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Unique insights

One of the most exciting uses for machine learning is to understand patterns in Big Data in a way that humans currently can’t – and then trigger concrete actions. For example, it can predict potential sales opportunities and then recommend actions to close deals.

Business acceleration

With machine-aided business processes and faster overall workflows, you can optimize business operations and your product and service offerings – so you can do and sell more while lowering back-office costs and TCO.

Better outcomes

AI and machine learning help to eliminate human error, improve the quality of outputs, and bolster cybersecurity – a must for financial service and other companies that need to protect sensitive information and comply with regulations.

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Machine learning use cases

The most popular examples of machine learning in action are consumer applications like recommendation engines and smart devices. But the technology also holds great promise for business-to-business (B2B) use cases. Here are two key areas we think the technology will really shine: 
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Smart business processes

Machine learning shifts traditional rules-based processes to intelligent ones that can discover new patterns in large, unstructured data sets – and make strategic predictions all on their own. It can also take on highly repetitive tasks such as checking invoices and travel expenses for accuracy.

Digital assistants and bots

Advances in AI technology suggest that self-learning algorithms may soon come to their own conclusions within certain parameters and develop context-sensitive behavior. Devices will be able to schedule meetings, translate documents, and take on other routine business tasks.
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Machine learning FAQs

How do you ensure accurate results?

False positives and bias in machine learning are things to watch out for – but they have relatively straightforward solutions. To facilitate machine learning accuracy:

  • Start with clean data sets and ensure input data is labeled and categorized correctly to minimize false positives
  • Consider potential biases inherent in your data – if it’s garbage in, it’ll be garbage coming out. Ask questions and create processes for evaluating algorithms to avoid this
  • Use the right algorithm training method for your goal (e.g., supervised for predicting the sales price of a home on known variables)
  • Complete thorough machine learning training to boost learning outcomes

For more information, read the blogs How AI Can End Bias and Unmasking Unconscious Bias in Algorithms.

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Can you trust the decisions that machines make?

The idea of machines taking over our lives and livelihoods has made for some great movies, but the reality is far less dramatic. That’s not to say that we should put blind faith in outcomes uncovered through the machine learning process. Here’s how you can keep AI on track to produce reliable results:  

  • Conduct a proof of concept so you feel confident in the decisions that are being made
  • Supervise processes and results and make adjustments as needed
  • Adjust confidence levels by applying business rules in algorithms
  • Include feedback mechanisms in your machine learning training process
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How can we prepare our data?

Access to large data sets and machine learning go hand in hand – so minimizing information silos is a critical first step:  

  • Integrate your enterprise data – from suppliers, partners, customers, and more – to give algorithms open access to all relevant data
  • Engage your Chief Data Officer in the machine learning process
  • Consider using a cloud platform that can process high volumes of data integrated from different data sources
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How will machine learning fit into the workplace?
Machine learning is already being welcomed into offices around the world. Its role? To assist humans with routine tasks. Advances in machine learning technology are opening up countless new scenarios, opportunities, and business models that will:
 
  • Result in higher paid jobs that emphasize creativity, problem solving, and knowledge work
  • Automate boring, repetitive tasks to make jobs more interesting (and fun!)
  • Always allow humans to retain control over the most strategic tasks and priorities 
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Will I require specialized skills to use machine learning?

In the past, you needed specialized talent to put machine learning into action: “Quants” who are educated in the language and methods and “translators” who could bridge the disciplines of data, machine learning, and decision making to reframe complex results into executable insights.

Not anymore. Now modern business applications take on the roles of data scientists. They feature user-friendly interfaces and integrated AI technology that let business users reap the rewards of machine learning – without extensive training, and often with the push of a few buttons.

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What about ROI?
Even though machine learning in B2B applications is still in its infancy, companies are already using it to automate transactions, detect fraud, further medical research, and save time and money. And when AI is built into cloud platforms and applications, you won’t need a costly custom build to get up and running.
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Explore SAP Leonardo Machine Learning

Build your intelligent enterprise with a machine learning platform and software that unite human expertise and computer insights. Discover how easy it can be to become an algorithm-driven business that lets you automate highly repetitive tasks, support strategic decision making, and turn data into smart actions.
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Extend your reach

Our machine learning technology taps the largest data pool in the world – leveraging SAP systems across 25 industries and 12 lines of business. With our market leadership, you can get insights not available anywhere else.

Integrate quickly

Integrate intelligent solutions into your systems quickly and simply – and accelerate ROI – with “out of the box” machine learning capabilities embedded directly into the SAP Cloud Platform and natively built into all of our software applications.

Do the impossible

Be first to market with AI-driven product innovations and business models that delight customers and drive revenue. And use technology that self-optimizes and re-learns to continuously improve business outcomes and shrink cost and risk.

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Machine learning applications, platform, and services

Get a glimpse into the future with our first wave of machine learning enabled enterprise applications, tools, and services developed in collaboration with co-innovation customers – now available through one intelligent platform that connects them in the cloud.
AI platform: Building your intelligent foundation

Create, run, consume, and maintain self-learning apps with ease – no data science skills required. SAP Leonardo Machine Learning Foundation connects developers, partners, and customers to machine learning technology through SAP Cloud Platform.    

  • Get quick access to intelligent automation applications
  • Access versatile horizontal and vertical business services and data
  • Use service APIs, embedded AI, and a global marketplace to quickly build smart apps
  • Develop on an open, scalable platform with an intuitive, modern user experience (UX)

Watch the overview video
Download the solution brief
Learn more about SAP Cloud Platform

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Finance: Automating payment matching

Automate labor-intensive invoice matching processes and give finance more time to focus on strategy and service quality – with SAP Cash Application. Our next-generation intelligent software uses machine learning to match criteria from your history and automatically clear payments.

  • Improve days of sales outstanding
  • Integrate with SAP S/4HANA to reduce TCO and time to value
  • Help shared services scale to meet growing business needs
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Fraud detection: Improving the accuracy of alerts

Zero in on potential fraud cases and boost the accuracy of your alerts with SAP Fraud Management, software that uses predictive algorithms to analyze your historical data.  

  • Focus on the cases with the highest likelihood of fraud and ROI
  • Integrate with SAP HANA to reduce TCO and time to value
  • Rely on models that update as fraud patterns evolve
  • Use a mix of custom and third-party algorithms optimized for your business
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Recruiting: Finding the best talent with intelligent job matching

Put the days of sifting through thousands of resumes behind you – with our intelligent job matching application. SAP Resume Matching uses machine learning to automate the screening process and zero in on the best candidates or jobs without bias.   

  • Fast-track the recruiting process to save time and effort   
  • Quickly find the right talent and reduce false positives   
  • Devote more time to corporate brand leadership
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Marketing: Logo and brand recognition

Better evaluate your advertising and sponsorship campaigns with SAP Brand Impact. Using advanced computer vision techniques, the application can automatically recognize logos in images and videos – giving your agency or production company accurate, timely insights into marketing ROI.

  • Leverage fast, near-real time brand analysis through an interactive interface that lets you audit all outputs
  • Rely on accurate analyses scalable to millions of hours of footage
  • Review outcomes second-by-second, compare and filter out brand assets, and view aggregated statistics
  • Combine data with your CRM and ERP software and website stats via a time-annotated impact indicator API
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Customer service: Gathering, analyzing, and responding to feedback

Accelerate customer service in your omni-channel front office. SAP Service Ticket Intelligence lets you efficiently process inbound social media posts, e-mails, and other channel interactions by automatically determining classifications, routing, and responses.

  • Improve service response times with automated processing
  • Integrate with SAP Hybris Service Cloud for faster time to value
  • Process more digital interactions without sacrificing quality
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Sales & marketing: Loyalty and retention

Anticipate customers’ behavior, such as product cancellations or renewals, with instant insights from transactional data and digital interaction points. SAP Customer Retention uses advanced machine learning to mine, predict, and capture leading churn indicators – all automatically. Based on the results and your company priorities, you can define and execute next best actions more efficiently.

  • Spot and classify interaction patterns
  • Detect dissatisfied customers, understand root causes, and act on timely predictions
  • Build customer loyalty with proactive retention strategies
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Getting started with machine learning

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Is it a good fit?

Machine learning is ideal for scenarios with complex rules and unknown elements, for making predictions on new rather than historical data, and for automating highly repetitive tasks. In other cases, rules-based programming is fine.

Take a crash course

Still unsure how to apply machine learning in an enterprise context? Our free OpenSAP course will guide you through your next steps: Identify the right problems, prepare data in complex landscapes, and discover use cases and prototypes.

8 best practices

Get exclusive access to eight best practices to jump-start your machine learning journey. Our practical guide helps demystify some of the complex areas of predictive modeling and analytics, explores automation strategies and solutions, and more.

Enlist experts

Our business transformation services group can help you get up and running faster. Align your people, processes, and technology – and use proven methodologies and services to quickly deploy the latest digital and machine learning technologies.
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