Google Maps & γειτονιές με δύσκολο parking…& έχει & συνέχεια

A new feature in Google Maps will help you find parking – or rather, it will mentally prepare you for how difficult it will be to find a parking space at your destination. Google says it’s now using “historical parking data” to calculate a parking difficulty score, which will be displayed in Google Maps’ directions card…

via Google Maps will now tell you how bad the parking is at your destination — TechCrunch

IBM -support for Google’s Tensorflow to PowerAI machine learning framework

PowerAI is IBM’s machine learning framework for companies that use servers based on its Power processors and NVIDIA’s NVLink high-speed interconnects that allow for data to pass extremely quickly between the processor and the GPU that does most of the deep learning calculations. Today, the company announced that PowerAI now supports Google’s popular Tensorflow machine… Read More

via IBM adds support for Google’s Tensorflow to its PowerAI machine learning framework — TechCrunch

Matibabu – Νέες Εξελίξεις στην Υγεία – ενάντια στην Μαλάρια

Matibabu, which is competing in our Hardware Battlefield at CES today, isn’t looking to cure any diseases. Instead, the Uganda-based company is looking to make it easier to diagnose malaria so those who are infected can get the right help faster. I actually first ran into Matibabu back in 2013 when the team competed in…

via Matibabu uses light to diagnose malaria — TechCrunch

Ορολογία της Ημέρας (T.O.D) – 16/12/2016 [Storage Resource Management]


SRM (short for Storage Resource Management) refers to the management of storage resources along with optimizing how the available drive space is utilized within a network.

SRM focuses on avoiding file duplication, determining space utilization across network servers, and improving the speed and efficiency of how storage resources are used within the network.

Ορολογία της Ημέρας (T.O.D) – 14/12/2016 [Cloud Engineer]

cloud engineer

A cloud engineer is an IT professional responsible for any technological duties associated with cloud computing, including design, planning, management, maintenance and support.

The cloud engineer position can be broken into multiple roles, including cloud software engineer, cloud security engineer, cloud systems engineer and cloud network engineer. Each position focuses on a specific type of cloud computing, rather than the technology as a whole. Companies that hire cloud engineers are often looking to deploy cloud or further their cloud understanding and technology.

Job listings on seek cloud engineers with at least three to five years’ experience with cloud — including open source technology, software development, system engineering, scripting languages and multiple cloud provider environments. Additionally, cloud engineers must have a background building or designing web services in the cloud.

Cloud engineers need to be familiar with programming languages including Java, Python and Ruby. Many companies looking to hire cloud engineers seek experience with OpenStack, Linux, Amazon Web Services, SoftLayer, Rackspace, Google cloud, Microsoft Azure and Docker. Experience with APIs, orchestration, automation, DevOps and databases like NoSQL are also important.

A cloud engineer should have a Bachelor of Science degree in computer science, engineering or another related field, but some companies prefer a Master of Science degree. Additional certifications may be required.

Ορολογία της Ημέρας (T.O.D) – 13/12/2016 [Ensemble Modeling]

ensemble modeling

Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

In predictive modeling and other types of data analytics, a single model based on one data sample can have biases, high variability or outright inaccuracies that affect the reliability of its analytical findings. Using specific modeling techniques can present similar drawbacks. By combining different models or analyzing multiple samples, data scientists and other data analysts can reduce the effects of those limitations and provide better information to business decision makers.

One common example of ensemble modeling is a random forest model. This approach to data mining leverages multiple decision trees, a type of analytical model that’s designed to predict outcomes based on different variables and rules. A random forest model blends decision trees that may analyze different sample data, evaluate different factors or weight common variables differently. The results of the various decision trees are then either converted into a simple average or aggregated through further weighting.

Ensemble modeling has grown in popularity as more organizations have deployed the computing resources and advanced analytics software needed to run such models. In addition, the emergence of Hadoop and other big data technologies has led businesses to store and analyze greater volumes of data, creating increased potential for running analytical models on different data samples.