As the amount of data that needs to be processed continues to increase, more and more IT teams are turning to cloud computing to help manage their large workloads. Workload Automation plays a vital role in managing virtual and cloud resources and can mean the difference between successful, cost-efficient cloud computing, and hidden-cost ridden operations. A Workload Automation solution that offers automated provisioning and deprovisioning of virtual and cloud-based resources, based on both historical and predictive analytics, can introduce a form of machine learning into your cloud environment and help you optimize your resource usage. The EMA Radar Report commends ActiveBatch Workload Automation on its standout cloud features, such as Smart Queue and Managed Queue, and its prebuilt integrations with VMware, Amazon EC2, Microsoft Azure, and System Center Virtual Machine Manger. The report states that these features and capabilities "make ActiveBatch a strong choice for anyone relying on hybrid or multi-cloud to optimize resource usage."
The analytics engine offers a turn-key, closed-loop, autonomous system that continuously monitors users, devices, applications, networks to detect anomalous or malicious behavior and offers precise actions to mitigate and prevent them, delivering the most secure workspace in the industry. Enterprises are rapidly adopting a variety of new paradigms -- mobile devices, bring your own device (BYOD), SaaS applications, and public clouds -- that boost employee productivity while offering more choice & flexibility. This, however, has adverse consequences on Security. The most notable one is that the traditional well-defined security perimeter around the data center is no longer valid and this renders traditional solutions aimed at defending that perimeter insufficient. Also, the attacks and the attack vectors are becoming highly sophisticated and the traditional threat detection techniques based on signatures and known patterns have limited effect.
Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.
Did You Want a Side of SLBS (Serverless BS) with Your Software or Hardware FUD? A few years ago a popular industry buzzword term theme included server less and hardware less. It turns out, serverless BS (SLBS) and hardware less are still trendy, and while some might view the cloud or software-defined data center (SDDC) virtualization, or IoT folks as the culprits, it is more widespread with plenty of bandwagon riders. To me what's ironic is that many purveyors of of SLBS also like to talk about hardware. Simple, on the one hand, there is no such thing as software that does not need hardware somewhere in the stack.
The Microsoft Data Science Virtual machine (VM) is a custom Azure VM based on Windows Server 2012 with several popular tools for data science modeling/development like: * SQL Server 2016 Developer Edition * Microsoft R server Developer Edition * Anaconda Python with Juypter notebooks * Visual Studio 2015 Community edition with language and Azure tools and * ML and Deep Learning tools like xgboost, CNTK, mxnet More information on how to use the VM can be found on the [documentation page](http://aka.ms/dsvmdoc). If are wondering about things you can do with the DSVM read this [How-To Guide to the Data Science Virtual Machine](http://aka.ms/dsvmtenthings). Here is a list of key software on the Data Science Virtual Machine and comparison between the Windows and Linux editions of the product.
The age of Knight Rider is upon us. As the Internet of Things (IoT) revs up the automotive industry, connected cars are becoming "devices on wheels" with in-vehicle systems connected to the Internet. At the same time, car manufacturers and software companies are redoubling their efforts to bring automated cars into widespread use. For example, Volvo announced a partnership with Microsoft to develop driverless cars for the consumer market. IoT not only will bring in new vehicle technologies, but also will completely revolutionize the car industry.