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.

Watson Natural Language Classifier (NLC) is a text classification (aka text categorization) service that enables developers to quickly train and integrate natural language processing (NLP) capabilities into their applications. Once you have the training data, you can set up a classification model (aka a classifier) in 15 minutes or less to label text with your custom labels. In this tutorial, I will show you how to create two classifiers using publicly available Airbnb reviews data. One of the more common text classification patterns I've seen is analyzing and labeling customer reviews. Understanding unstructured customer feedback enables organizations to make informed decisions that'll improve customer experience or resolve issues faster.

The moment IBM Watson played Jeopardy on TV almost a decade ago was the time AI burst onto the scene. It was a breakthrough marketing moment. Over the last decade, IBM Watson has remained the go-to player in Artificial Intelligence as the industry grows. Every year IBM holds their Think conference where they pull together thought leaders from companies, governments, think tanks and soon. This has become the AI super-show.

See how businesses can survive and thrive using AI in Marketing and across the Organization.

Care and Feeding is Slate's parenting advice column. In addition to our traditional advice, every Thursday we feature an assortment of teachers from across the country answering your education questions. Have a question for our teachers? Email askateacher@slate.com or post it in the Slate Parenting Facebook group. This week's Ask a Teacher panel: Matthew Dicks, fifth grade, Connecticut Cassy Sarnell, preschool special education, New York Carrie Bauer, middle and high school, New York Amy Scott, eighth grade, North Carolina My fourth-grade daughter is a joy to be around, a good friend, and a well-behaved student.

According to Gartner Inc. AI is going to be one of the biggest the trends. It will be the number one driver of business change with reports suggesting the market size to reach $20 billion by 2022. While with over 400 people killed in road accidents every day and over 1.3 million people killed in India over the past one decade. There is a dire need to improve road safety in India. Artificial Intelligence can prominently play a role in bridging the gap by improving efficiency in driving patterns.

As data people, we very typically spend a great deal of time summarizing our findings to stakeholders in a clear, concise and impactful way. Often times, due to the lack of infrastructure, we end up using presentation files with chart images. This can become a real pain when we need to make modifications or when the analysis needs to "live on". Typically, this is where BI (business intelligence) or dashboard tools shine. Unfortunately, this can be a major stumbling block for smaller shops who rely on a lot of local analysis and may not have the budget for a BI tool.

Workday held its European Rising conference last year. One of the key themes from the event was how it is embedding AI into its solutions. Having spoken to Chano Fernandez, Co-President Workday early in the week, we also spoke to Barbry McGann, SVP Product Management at Workday later in the conference. The conversation centred around the main message that Workday delivered at its latest conference, AI. One of its recent product innovations was Skills Cloud.

The TDA models have by far the richest functionality and are, unsurprisingly, what we use in our work. They include all the capabilities described above. TDA begins with a similarity measure on a data set X, and then constructs a graph for X which acts as a similarity map or similarity model for it. Each node in the graph corresponds to a sub-collection of X. Pairs of points which lie in the same node or in adjacent nodes are more similar to each other than pairs which lie in nodes far removed from each other in the graph structure. The graphical model can of course be visualized, but it has a great deal of other functionality.

Decision Optimization is now available in the Watson Studio ecosystem with a seamless integration of the CPLEX solvers in the Python runtime environment. Watson Studio now provides everything you need to describe your data, gain insight, and make an optimal decision in the very same ecosystem. Get started right away and learn how to make more intelligent marketing and targeting decisions. Decision Optimization is a subset of data science techniques frequently used for prescriptive analytics. Most documented data science use cases are dedicated to revealing or predicting unknown or future data that is not under your control.