Using machine learning the smart way

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Organizations should grow like human beings: if they don’t invest in developing their knowledge and expertise they will stop learning and become old very fast – lacking the right insights and taking the wrong decisions. This is where machine learning helps. Machine learning isn’t new, but it is often applied the wrong way.

So how to get it right?

Machine learning is a technique letting you take past actions into account repeatedly, by applying smart algorithms to your data. The more data you feed the algorithms, the smarter they become: they turn into true experts, offering your organization actionable insights in split seconds. But the starting point for all this is understanding your business and your data. As a data scientist you first need to know what your organization’s business goals are, what data you need and what data you have. Only then can you apply machine learning properly – and things will get really interesting.

This means that you need the right tools, as well as an infrastructure that helps you collaborate with everyone in your organization involved in the process. The new IBM Data Science Experience (DSx) platform addresses all these requirements.
It’s a powerful cloud solution for optimizing business decisions, including tools for predictive analytics, prescriptive analytics and machine learning, and in particular: IBM Watson Machine Learning.

The DSx platform and Watson Machine Learning will help you improve models over time and simplify model management, while working alongside app developers, data engineers, business analysts and other data scientists. Applying machine learning is a matter of four steps with this platform: preparing your dataset, training the model you want to use, evaluating the model, and deploying it as a web service in the cloud. You can do all these things in your notebook or in real-time, just as you prefer.

The speed at which you can perform analyses with Watson Machine Learning is amazing: you can analyze all your databases in minutes rather than months. The latest capability of this advanced solution for machine learning is scoring. This unique feature gives you scores to gain insights into just how accurate your algorithm really is. It can be applied by banks for instance, to determine transaction-based risks based on customers’ spending histories, or by governments to detect non-compliance or tax evasion. There are many more use cases to think of in other industries. For example in retail, offering personal recommendations to customers based on historical information and previous purchases. Or, in the case of transportation, making routes more efficient by predicting demand patterns. The possibilities are endless.

Want to know more about making your organization smarter each day the easy way?

Take a look at IBM Data Science Experience.