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Showing posts from August 24, 2017

The growing popularity of wearable cameras is encouraging manufacturers

Steadily, one can observe that, the growing popularity of wearable cameras is encouraging manufacturers to invest in research & development for creating better products. In effect, Manufacturers have been observed making significant investments to simultaneously develop new products in an effort to enhance user experience. Researchers are focusing on innovating reliable and cost-effective products. Among key vendors, we have GoPro Inc., Drift Innovation Ltd, and Garmin Ltd.

Sports and adventure within the global wearable camera market

Generally, the wearable market is segmented based on varied types of wearable cameras, such as head mount, body mount, and ear mount & smart glass. Based on applications, the market has been segmented into sports & adventure, security, healthcare, and industrial. According to Research and Markets, the sports & adventure sector is the largest application sector in terms of size and is expected to dominate the market over the forecast period, owing to the rising popularity of sports and adventure activities.

Here is what explains the growth and penetration of wearable cameras

Steadily, one can observe that, the growing demand for convenience and exceptional experience of cameras is increasing the growth and penetration of wearable cameras. The growing demand for smartphones, easy accessibility of the internet, and high-speed data networks have resulted in the increasing usage of wearable cameras. According to Research and Markets, the growing adoption of wearable cameras in the security and medical sectors is anticipated to revolutionize the market by 2025. The firm also reveals that, the global wearable camera market is expected to reach USD 10.9 billion by 2025, according to this new report.

Drones (UAVs) application in building and construction inspection

Building and construction is a complex industry where regular inspection and facility maintenanceis at the core of stakes. In effect, use of drones (UAVs) in building inspections and energy audits represent significant area for support of energy efficiency technologies that could help achieve reductions in energy consumption and carbon emissions. Drones (UAVs) could perform inspection work of buildings and construction processes quicker, safer and more cost effectively than traditional methods. According to Research and Markets, with drones (UAVs) will be easier to provide emergency response and inspect damaged roofs, collapsed buildings, and other difficult to reach places. This could prove especially valuable after severe storms, when technicians are often unable to assess damaged structures or evaluate claims.

PostGIS, Data type, Language, Miscellaneous within PostgreSQL

As a relational database, PostgreSQL is steadily billed as the open-source solution of choice for a wide range of workloads. The following 19 extensions, across four categories are now actionable: PostGIS: better support for geographic applicationsData type: a variety of new data typesLanguage: enhanced functionality with new processing languagesMiscellaneous: text search, cryptographic capabilities and integer aggregators, to name but a fewFor those who are unfamiliar, an extension is a piece of software that adds functionality, often data types and procedural languages, to PostgreSQL itself. If you already have a Cloud SQL for PostgreSQL database instance running, you can enable one or more of these extensions.

Concepts of Dark launches are useful when you want to launch a new version of an existing service

I have a pleasure to recall that, Adrian Hilton, Customer Reliability Engineer in its first part of this seriesintroduced us to the concept of dark launches.
In fact, in a dark launch, you take a copy of your incoming traffic and send it to the new service, then throw away the result. Dark launches are useful when you want to launch a new version of an existing service, but don’t want wrong surprises when you turn it on.

Accurately analyze phone call conversations between two individuals with real-time speech-to-text transcription

It is important to recall that, businesses using the Google Cloud Speech API improve speech recognition for everything from voice-activated commands to call center routing to data analytics. Word-level timestamps let users jump to the moment in the audio where the text was spoken, or display the relevant text while the audio is playing. You can find more information on timestamps here. Now with Google Cloud Speech API timestamps, we can accurately analyze phone call conversations between two individuals with real-time speech-to-text transcription. The ability to easily find the place in a call when something was said using timestamps makes Cloud Speech API much more useful and exciting.

Edge data, Content delivery, streaming, security and load-balancing with Fastly Edge cloud platform

For those who are unfamiliar, FastlyEdge cloud platform allows web applications to better serve global users with services for content delivery, streaming, security and load-balancing. The technology improves response times for applications built on Google Cloud Platform (GCP). Fastly now supports: streaming its logs to Google Cloud Storage and BigQuery, for deeper analysis. In fact with the BigQuery integration; they can now stream real-time logs to Google Cloud Storage and BigQuery, allowing companies to analyze unlimited amounts of edge data.
You can contact us with any questions.

Experiment your own Machine Learning (ML) models, by choosing TensorFlow

Steadily, one can observe that, companies are starting to experiment with their own ML models, and a lot of them are choosing TensorFlow.
For those who are unfamiliar, TensorFlow is open source, you can run it locally to quickly create prototypes and deploy fail-fast experiments that help you get your proof-of-concept working at a small scale. You can take TensorFlow, your data, and the same code and push it up into Google Cloud to take advantage of multiple CPUs, GPUs or soon even some TPUs.
Google have published a pair of solution tutorials to show you how you can create and run a distributed TensorFlow cluster on Google Compute Engine and run the same code to train the same model on Google Cloud Machine Learning Engine.

Running a gem server run on Google Cloud Platform (GCP) has a lot of advantages

It is important to recall that, running a gem server run on Google Cloud Platform (GCP) means that:
App Engine can autoscale the number of instances based on CPU utilization to minimize the amount of maintenance for the gem server. Having the gem serveron Google Cloud Platform (GCP) also allows you to use existing cloud infrastructure such as Stackdriver Logging, Cloud Storage and direct access to the underlying VM running the gem server for fine-grained control.
The gem server can store an unlimited amount of public and private gems allowing an unlimited amount of users with the correct permissions to access it. In fact flexibility and customization is at the core of stakes.

Now you can roll your own private Ruby gem server on Google Cloud Platform

This is great news for organizations that build libraries with proprietary business logic or that mirror public libraries for internal use.
For those who are unfamiliar, in Ruby, these libraries are called gems, and until recently, there wasn't a good hosted solution for serving them. For Ruby in particular, many developers found themselves building their own custom solutions or relying on third parties such as Gemfury. In fact, recently they releasedgoogle-cloud-gemserver gem, making it possible to deploy a private gem server to Google Cloud Platform (GCP) with a single command: $ google-cloud-gemserver create --use-proj [MY_PROJECT_ID]