Written by Alexander Raginsky Introduction There has been a renewed interest in machine learning in last few years. This revival seems to be driven by strong fundamentals – loads of data being emitted by sensors across the globe, with cheap storage and lowest ever computational costs! However, not every one around understands what machine learning is. Here are a
Written by Alexander Raginsky These predictions were published by the International Institute for Analytics (IIA). They produced a nice infographics, featured below, and re-tweeted many times by various bloggers, using the hash tag #2015Analytics. Other interesting predictions include those by Tableau, those by Pivotal, as well as my own predictions. Here are IIA’s predictions for 2015, in plain text:
Written by Alexander Raginsky We’re living in a sea of information, bathing in a firehose of data from across our businesses. We know we need to use it, but the question is how? How can we get value from that information, and at the same time get ready for the faster, bigger, deeper streams that are going to come
Written by Alexander Raginsky In the near future, the following major trends are expected in Big Data analysis: Big Data technology will be based on a mixture of cloud and on-premises computing. Many corporations start migrating from in-house database infrastructures to cloud-based data warehouses. Distributed frameworks for data analytics like MapReduce are turning into distributed managers of resources and
Written by Alexander Raginsky Amazon.com is boosting its artificial-intelligence chops. The world’s largest retailer by market capitalization is rolling out its sophisticated big-data-crunching platform for developers in Europe, and is also hiring scientists for its research teams in New York and Berlin who specialize in getting machines to do things like make sales predictions and predict fraud. Machine learning
Written by Alexander Raginsky Friends, I will attempt to introduce this unique concept, its advantages, unique features, actual case studies through a series of posts. Here is the first of the series: Digital Experience of an user (DX) on a web site or a mobile app or a social media site is very subjective and is attempts are being
Written by Alexander Raginsky Enterprise software company Workday is launching a venture fund aimed at early to growth stage startups that are leveraging machine learning and data science to solve enterprise-level problems. In conjunction with this announcement, Workday is also naming the first four startups it has invested in. Run by Adeyemi “Ade” Ajao, the company’s vice president for
Written by Alexander Raginsky Machine learning deals with hundreds of algorithms that have various modifications. When selecting an appropriate class of algorithms and an algorithm within the class, you should closely consider your problem, define what you should measure or predict and which tools you are going to use for this purpose. A possible set of the best machine-learning
Written by Alexander Raginsky Big data is now a buzzword. Many developers work hard to improve the processing of large amounts of data and many data scientists work on data analysis problems. Nevertheless not all is well in this field. Big data cause big problems: Insufficient data integration. Data are stored in large warehouses and different groups of specialists
Written by Alexander Raginsky Microsoft today announced Cortana Analytics Suite, a new package of data storage, information management, machine learning, and business intelligence software in a single convenient monthly subscription. Microsoft’s Cortana personal digital assistant, until now available to consumers on mobile devices and PCs, is part of the new package, designed to give non-technical people a simple interface
