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Microsoft xCloud: Azure Powered Game Streaming

Microsoft is building a game streaming network to unlock console quality gaming on any device. Microsoft has realized that not all gamers are going to buy an Xbox. Instead of focusing on building games that can only be played on the Xbox game console, Microsoft is working on a cloud based game streaming service where anyone, on any device, anywhere in the world can play games that would traditionally be made available only to Xbox gamers. The service Microsoft is building is called Microsoft xCloud; and it’s being built on top of Microsoft Azure. Microsoft xCloud streaming to a smartphone, with game play using an Xbox controller. Cloud Considerations When building a game streaming service, there are many things to consider around game quality and usability. For instance, the latency needs to be low enough that a button click doesn’t take too long to register. Imagine firing a weapon in an...

A Digestible Action Plan for Startups’ Cybersecurity Success

It’s never too early for a start-up business to begin to strategize and operationalize its cybersecurity goals–in fact, it’s a necessary prerequisite for high-yield growth. And yet, with all the high velocity activity and rapid decision-making that characterizes most startups’ early existence, it can be easy to overlook some of the critical prophylactic steps that must be taken to safeguard a nascent company’s value potential. The importance of this cannot be overstated, given that the harm to a startup’s reputation and brand name can be existential if proper controls are not in place. A recent Forbes CommunityVoice  article  by start-up founder Isaac Kohen offers some helpful starting points for businesses of all sizes to keep in mind. The major takeaways are summarized below, with additional perspective added. Growing a CyberSecurity Culture from Day One. A critical reminder for all is that cybersecurity is not at heart an infrastructure issue—it’s a cultural one....

Data Science Central Monday Digest, July 8

Monday newsletter published by Data Science Central. Previous editions can be found  here . The contribution flagged with a + is our selection for the picture of the week. To subscribe,  follow this link .   Featured Resources and Technical Contributions  Featured Articles Picture of the Week Source: article flagged with a +  From our Sponsors To make sure you keep getting these emails, please add   [email protected]  to your address book or whitelist us. To subscribe, click  here . Follow us:  Twitter  |  Facebook . Views: 214 Tags: < Previous Post Hello, you need to enable JavaScript to use Data Science Central. Please check your browser settings or contact your system administrator. Most Popular Content on DSC To not miss this type of content in the future,  subscribe  to our newsletter. Other popular resources Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More Follow us:  Twitter  | ...

Top 10 Data Science Use Cases in Energy and Utilities

The energy sector is under constant development, and more of significant inventions and innovations are yet to come. The energy use has always been involved in other industries like agriculture, manufacturing, transportation, and many others. Thus these industries tend to enlarge the amount of energy they consume every day. Energy seems to be very demanding in terms of new technologies application and development of new energy sources. The rapid development of the energy sector and utilities directly influences social development. People are now facing challenges of smart energy management and consuming, application of renewable energy sources and environmental protection. Smart technologies play a crucial role in the resolution on these matters. In this article, we attempted to present the most vivid data science use cases in the industry of energy and utilities. Failure probability model  Failure probability model has won its place in the energy industry.  The efficiency of the machine...

How Retailers Use Artificial Intelligence to Innovate Customer Experience and Enhance Operations

Digitalization influences how businesses operate and build and maintain relationships with customers. With the internet open 24/7, consumers can save time and shop online at their convenience. In 2017, global eCommerce sales accounted for 10.2 percent of all retail sales ($2.3 trillion US). This figure is projected to reach 17.5 percent in 2021. Revenue from eCommerce sales is expected to grow to $4.88 trillion US. eCommerce share of total retail sales worldwide from 2015 to 2021. Source: Statista Physical stores still have the lion’s share of sales, but the growing demand for online experiences shouldn’t be ignored. To remain competitive, retailers must allow in-store customers to enjoy the benefits of online shopping. Fast checkout, personalized recommendations, or instant access to customer care at any time are a few services that can be implemented with the help of artificial intelligence. For this article, we discussed current and potential applications of AI in retail, as well as...

Data Quality Case Studies: How We Saved Clients Real Money Thanks to Data Validation

Machine learning models grow more powerful every week, but the earliest models and the most recent state-of-the-art models share the exact same dependency: data quality. The maxim “garbage in – garbage out” coined decades ago, continues to apply today. Recent examples of data verification shortcomings abound, including JP Morgan/Chase’s  2013 fiasco  and this lovely list of  Excel snafus .  Brilliant people make data collection and entry errors all of the time, and that isn’t just our opinion (although we have plenty of personal experience with it); Kaggle  did a survey  of data scientists and found that “dirty data” is the number one barrier for data scientists.   Before we create a machine learning model, before we create a Shiny R dashboard, we evaluate the dataset for a project.  Data validation is a complicated multi-step process, and maybe it’s not as sexy as talking about the latest  ML models, but as the data science consultants of...

Lightweight but effective way of documenting a group of Jupyter Notebooks

My app Qubiter has a folder full of Jupyter notebooks (27 of them, in fact). Opening a notebook takes a short while, which is slightly annoying. I wanted to give Qubiter users the ability to peek inside all the notebooks at once, without having to open all of them. Qubiter’s new SUMMARY.ipynb notebook allows the user to do just that. SUMMARY.ipynb scans the directory in which it lives to find all Jupyter notebooks (other than itself) in that directory. It then prints for every notebook it finds (1) a hyperlink to the notebook, and (2) the first cell (which is always markdown) of the notebook. This way you can read a nice, automatically generated summary of all the notebooks without having to open all of them. If you find a notebook that you want to explore further, you can simply click on its link to open it. Here is the code...

Predicting Hotel Cancellations with Support Vector Machines and SARIMA

Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices. Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly. To investigate how machine learning can aid in this task, the ExtraTreesClassifer, logistic regression, and support vector machine models were employed in Python to determine whether cancellations can be accurately predicted with this model. For this example, both hotels are based in Portugal. The Algarve Hotel dataset available from Science Direct was used to train and validate the model, and then the logistic regression was used to generate predictions on a second dataset for a hotel in Lisbon. Data Processing At...

How To Choose An NLP Vendor For Your Organization

Views: 129 Tags: Hello, you need to enable JavaScript to use Data Science Central. Please check your browser settings or contact your system administrator. Most Popular Content on DSC To not miss this type of content in the future,  subscribe  to our newsletter. Other popular resources Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More Follow us:  Twitter  |  Facebook Original link Original author: Data Science Central

USER.NOTIFICATION - WARNING: Notification , recorded at 2019-07-05T06:00:42Z

USER.NOTIFICATION - WARNING: Notification recordedat 07/05/2019 6:00:42 AM UTC Title: Bad Practice Alert Message:[BAD_PRACTICE] Clear Change Flag Shape executing with no documentsin Clear Quota Change Flag(execution-0ebcceda-91b0-40cb-9c9d-d495440bdd63-2019.07.05)Environment: Standard Production Classification: Production Original link Original author: Boomi AtomSphere RSS Feed