Imagine you are living in a townhome complex where expenses like water, landscaping and parking lots are managed by the Homeowners Association (HOA). All is well in the world until one day, the HOA instructs you to get rid of your hot tub because they have found that the meter you share with your neighbor is consuming significantly more water than any other townhome in the complex and they suspect because you have a hot tub, it must be your fault! If this sounds a little too specific to be a consequence (or a particularly nasty case of hot tub jealousy), that is because this nightmarish situation was exactly what my friend Sadie faced in 2017. Sadie fought the good fight and persevered to where she ultimately kept her hot tub after proving her neighbor had not one, but TWO leaking toilets that had been gradually getting worse and worse (for YEARS). Upon hearing of this -- as an unabashed data nerd -- I was of course thinking, "how could data from Sadie's meter have been used to detect and prevent this!?"

This release was all about improving and enhancing toolkits' abilities to provide insights into your data, including a brand new outlier detection assistant, an update to our Machine Learning examples showcase page, an upgrade from Python 2.x to Python 3.x and a new System Identification algorithm. Outlier detection is by far the most popular use case in the industry. We constantly seek ways to offer a simple, yet rich and accurate way of helping you find outliers in your data, evaluate it and deploy it in your Splunk environment. It is not only smart by not having prejudice against your data's statistical characteristics, but also charming with a new set of custom visualizations available. With Python 2.7 coming to its end of life, Splunk 8.0 is migrating to Python 3.7 and so is the Splunk Machine Learning Toolkit.

Do you want to reap the benefits of the advances in the fields of artificial intelligence and machine learning? Join InterSystems Sales Engineers, Sergey Lukyanchikov and Eduard Lebedyuk, for the Machine Learning Toolkit for InterSystems IRIS webinar to find out how InterSystems IRIS can be used as both a standalone development platform and an orchestration tool for predictive modelling that helps stitch together Python and other external tools. See additional resources: 1. Discuss the webinar on Developer Community – Python Gateway Part I: Introduction: http://bit.ly/2WEknF7

Machine learning is crucial to the next industrial revolution. As equipment and supply chains join the Industrial IoT (IIoT), the flood of data can overwhelm already-busy human supervisors--creating an urgent need for self-regulating automation. Each generating facility contains a complex, interdependent ecosystem of equipment and infrastructure. Collectively, these systems generate enormous amounts of data--up to 1.5TB per day. These staggering volumes of data exceed human capabilities but fit neatly into the wheelhouse of machine learning.

The developers at the data analytics company Splunk have been keeping busy. On Tuesday the company announced expanded machine learning capabilities across its entire product line, with new releases of five products. The timing of the releases might have something to do with the fact that they were announced at .conf2017 in Washington, D.C. -- otherwise known as the 8th Annual Splunk Conference -- because that's the way the tech world rolls. Splunk is a San Francisco based software company that's been around since 2003. It's stock-in-trade is software for searching, monitoring, and analyzing machine-generated big data by way of a Web-style interface.

Gensim users and developers have shaped the newest release 0.13.0 through their comments, requests and Python code contributions. The release includes new features like Word Movers Distance (WMD) and offers a new tool which is used to tune Topic Models. RaRe Technologies today announced a major update of the software package Gensim, an open source machine learning toolkit for understanding human language. Gensim users and developers have shaped the newest release 0.13.0. This release includes new features like Word Movers' Distance (WMD)*, a novel distance function between unstructured text documents, plus new Tutorials and Quickstarts.

Distributed machine learning has become more important than ever in this big data era. Especially in recent years, practices have demonstrated the trend that bigger models tend to generate better accuracies in various applications. However, it remains a challenge for common machine learning researchers and practitioners to learn big models, because the task usually requires a large number of computation resources. In order to enable the training of big models using just a modest cluster and in an efficient manner, we release the Microsoft Distributed Machine Learning Toolkit (DMTK), which contains both algorithmic and system innovations. These innovations make machine learning tasks on big data highly scalable, efficient and flexible.