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Business users have access to more data, and more powerful tools for harnessing the power of that data, than ever before. According to the IDC's most recent five-year forecast, the global datasphere has grown explosively in recent years, rocketing from less than 20 zettabytes in 2016 to 62.2 zettabytes in 2020 — and is expected to reach more than 180 ZB within about three years.

Industry 4.0 technologies like IoT have helped power this surge, as thousands of sensors distributed throughout factories, warehouses and other facilities continually send data to the cloud for analysis.

All of that data unlocks seismic new opportunities for understanding. Cloud-based analytics, artificial intelligence and machine learning make it possible to sift through and analyze vast amounts of data to identify patterns, find valuable insights and make predictions about future opportunities and challenges. Used effectively, machine learning can help you figure out not only what's happening now, but what's about to happen and how you can most successfully respond to it.

So let's talk about machine learning — what it is, where it can take you and how to get there.

Setting the Stage for Machine Learning

Before you jump headfirst into machine learning, there are some important steps you need to take to prepare the way. First and foremost, you need a solid analytics foundation. Machine learning and artificial intelligence are exciting, potentially transformative technologies — but they depend on effective data management. So before you can deploy machine learning successfully, your organization needs to map out a data journey that ensures you'll be ready to take advantage of it.

  1. The first step is an assessment to see where you currently are, so you can visualize where you'd like to go.
  2. Making sure you're collecting the right data, as well as handling it, processing it, and storing it safely are all necessary prerequisites.
  3. Getting data out of silos and making it available to business users is a key step, too, so that it can be mined for the critical business insights that can take you to the next level. Making use of data management tools that can automate data collection and analysis can be a help with this part of the journey.
  4. User adoption can be a challenge, too — so in order to create a more data-driven organization you need to plan for training to get your team up to speed on the benefits of these new tools, and in the right mindset to get the most from them.

Sometimes developers and other IT professionals are intimidated by the idea of machine learning, thinking of it as scientific discipline that requires deep mathematical expertise. And that's certainly true of theoretical machine learning. But applied machine learning allows IT professionals to use pre-made tools that automate the process of setting up algorithms, configuring them and putting them in motion.

What Is Machine Learning — and What Can It Do for You?

Machine learning makes use of algorithms and statistical models to create systems that can automatically learn, adapt and improve over time based on data and experience. As the algorithms identify patterns in the data, this enables applications like predictive analytics and forecasting.

The business uses for machine learning fall primarily into two broad groupings:

  • Making predictions: Models are built and trained and then iteratively processed so that they become increasingly more accurate at forecasting as they receive more data.
  • Pattern discovery: Algorithms are used to detect natural groupings within the data, such as clustering (where two or more variables interact) or anomaly detection.

There are other applications as well, like collaborative filtering and contextual bandits, but for business purposes prediction and discovery tend to dominate.

On a practical level, what can you use machine learning for? Here are some of the possibilities:

  • Forecasting
  • Recommendations
  • Equipment monitoring
  • Churn analysis
  • Fraud detection
  • Anomaly detection
  • Ad targeting
  • Image detection and analysis
  • Spam filtering

A common example of machine learning algorithms in action would be when your bank notifies you of possible fraud on your account. That means a fraud-detection algorithm detected behavior that was anomalous based on your usual patterns — which allowed the bank to take action to protect you.

Forecasting demand is another very common machine learning application. You can use it to analyze the profitability of possible ventures, to optimize business process, to optimize prices or to predict when equipment might malfunction so you can engage in preventative maintenance. You might look at worker churn — predicting how long employees are likely to stay with your company, and what the risk is of losing them to another organization.

What Answers Can Machine Learning Provide?

Machine learning can be used to answer critically important questions for your organization by deploying different kinds of algorithms. Here are some of the most commonly used.

  • Classification: "Is this A or B?" For example, you might use a classification algorithm to determine whether an email is spam or not-spam.
  • Regression: "How much or how many?" Creates forecasts by estimating the relationships between values. You might use this to determine what air fares or the price of an automobile should be.
  • Anomaly Detection: "Is this weird?" Anomaly detection looks for a deviation from usual patterns within the data.
  • Recommendations: "Will they like it?" Predicts whether someone will be interested in something based on past behavior.
  • Clustering: "How is this organized?" Separates similar data points into intuitive groups.
  • Reinforcement Learning: "What should I do next?" Assists with decision-making by identifying the best steps to take in order to maximize rewards.
  • Text Analytics: "What info is in this text"? Used to derive high-quality info from text — and also to process it with cleanup operations like standardizing capitalization or removing stop-words.
  • Image Classification: "What does this image represent?" Analyzes and classifies visual images.

Rapid Deployment with Azure Machine Learning

Machine learning is a highly competitive space, so there are lots of options out there — including both open-source tools and major platform solutions.

When you're ready to move forward, Azure Machine Learning has the advantage of elements that will be familiar to users of other Azure tools, which may help you get up and running faster.

Azure Machine Learning provides you with a graphical interface where you can rapidly build, train and deploy models. It allows you to run multiple experiments at the same time to determine which algorithms are most effective for your needs. It offers more than 100 easily configured models for data prep, training and evaluation, with extensibility through R and Python — and it allows for serverless training and deployment.

Watch the Webinar for a Closer Look

Interested in learning more? Watch a presentation from DynamicsCon, where I explore this topic in greater detail — including a demo of Azure Machine Learning.

Webinar: Machine Learning in 40 Minutes - Finance and Operations


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