Machine learning uses statistical analysis to generate prediction output without requiring explicit programming. It employs a chain of algorithms that learn to interpret the relationship between datasets to achieve its goal. Unfortunately, most data scientists are not software engineers, which can make it difficult to scale up to meet the needs of a growing firm. Data scientists can easily handle these complications thanks to Machine Learning as a Service (MLaaS). Machine Learning as a service (MLaaS) has recently gained much traction due to its benefits to data science, machine learning engineering, data engineering, and other machine learning professionals.

Watch this video to see how to use Watson Machine Learning and IBM Watson Studio to create a data flow using IBM SPSS Modeler to predict chronic kidney disease. Find more videos in the IBM Watson Data and AI Learning Center at http://ibm.biz/learning-centers.

With version 2.1, IBM Machine Learning for z/OS is rebranded to IBM Watson Machine Learning for z/OS. It offers a hybrid cloud approach to model development and model deployment lifecycle management and collaboration that is designed to help organizations innovate and transform on an enterprise scale. It helps data scientists more quickly develop, deploy, and monitor behavioral models that continually learn as new data is introduced. IBM Db2 AI for z/OS, V1.2, a separately licensed product, uses machine learning to improve the operational performance of Db2 for z/OS systems. Watson Machine Learning for z/OS, V2.1 is a key component for operationalizing machine learning models on z/OS. It provides the ability to deploy models on z/OS that were developed and trained in the cloud, on IBM Z or on non IBM Z platforms. This provides greater deployment flexibility through a new architecture where model management, administration, and scoring services install and execute on z/OS. The new version includes capabilities that were previously separately available through IBM Open Data Analytics for z/OS to help simplify the acquisition, installation, and configuration of the product. Watson Machine Learning for z/OS provides an environment that fosters collaboration to enable innovation and transformation on an enterprise scale.

If you want to learn how to use Python notebooks on IBM Bluemix to do machine learning with your SAP data, you may have a look at this blog entry. It provides detailed step-by-step explanations how to accomplish this.

Today we are excited to announce that IBM Watson Machine Learning is generally available. In the last 12 months we invited more than 1,000 IBMers to try the close Beta of the Watson Machine Learning (WML) service and more than 150 clients. We collected a lot of feedback from all of you. Today we are very excited to roll out the next set of features of the service, some of them as GA and others as Open Beta. We've carefully read all your comments in emails, Slack messages, survey answers, etc.

When you throw an event hoping to draw 400 people but an audience of 29,000 shows up, do you think it's a good sign indicating you're onto something? That incredible interest is what happened in Beijing the week of 28 November 2016 at the International Summit on Machine Learning and Industry Application. The event's 20 speakers gathered from across industries and academia to offer their insights about machine learning trends and new directions. For the keynote address, Dinesh Nirmal, vice president, analytics development, at IBM, teamed up with Kent Ting, vice president, IBM Analytics Global Consulting Group, at IBM. Nirmal and Ting talked about the IBM focus on machine learning and the company's efforts to enable developers in China and elsewhere. Of particular interest to the audience was their demo of IBM Watson Machine Learning, a full-service IBM Bluemix platform offering.