Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs (2016)

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Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs (2016)

Eugene Melnikov

performance-ruby-rails-sinatra-rack

The Ruby ecosystem is constantly evolving. There have been many changes in the engineering world since our comparison of Ruby frameworks in 2014. During the two years, we received a few requests from fellow engineers asking for an updated benchmark. So, here is the 2016 edition with more things tested.

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TensorFlow and OpenPOWER Driving Faster Cancer Recognition and Diagnosis

Roger Strukhoff

tensorflow-and-openpower-driving-faster-cancer-recognition-and-diagnosis-v16

At IBM Edge 2016, a team of developers and data scientists presented a practical study that evaluated the efficiency of training a TensorFlow model in a distributed mode. A use case featured high-resolution images of lymph nodes used for possible cancer detection.

Relying on a distributed model of TensorFlow and high-performing nature of the OpenPOWER infrastructure, the demonstrated system can accelerate medical data analysis—depending on the number of GPUs and nodes in its cluster. The particular subjects of the research were how training time decreases when the cluster grows and whether the accuracy of the results is affected by the distributed nature of the computations. Read this post for brief results and technical details.

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How to Use Snap Packages when Collecting IoT Data with Predix Time Series

Ilya Drabenia

canonical-snaps-ge-predix-time-series-demo-app

Snaps, designed by Canonical, are intended for packaging applications and their dependencies, along with the instructions for running these applications. In this article, we focus on how to write time-series data from your Linux environment to the Predix Time Series service using a snap.

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Creating Your First Serverless App with AWS Lambda and the Serverless Framework

Ilya Drabenia

serverless-architectures

Serverless architectures are one of the newest trends in computing that brings reduced infrastructure and development costs to the table. According to this approach, an application is split into multiple functions with each of them deployed separately.

Here, we demonstrate how to create a serverless application and deploy it to AWS Lambda, as well as explain some of the architecture basics.

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Automating Customer Interactions with IBM Watson Conversation and Bluemix

Ilya Drabenia

ibm-bluemix-watson-conversation-service

Virtual agents and chatbots can significantly optimize the operations of customer service departments. Trained to answer questions frequently asked by end users, they help organizations to automate a variety of customer interactions, engaging team members only in complex situations.

In this post, we explain how to work with the Watson Conversation service in Bluemix for creating chatbots.

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Using Predix UI Components: Page Layout and Login

Nick Herman

using-predix-ui-components-page-layout-login-v1

Predix provides several UI components that help you to build web apps faster and easier. The components are implemented with Polymer, a lightweight library built on top of WebComponents, and can be integrated with other Predix services, such as Time Series.

In this post, we explain the basics of using Predix UI, with focus on page layout requirements and the px-login component.

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Introducing Cloud Foundry CLI Plugin for Managing Predix Analytics Catalog

Stas Turlo

cloud-foundry-cli-plugin-for-managing-predix-analytics-catalog

Analytics Catalog is a service in GE Predix that provides a repository for hosting and exposing analytic assets. Today, we’re introducing a Cloud Foundry command-line interface (CF CLI) plugin that enables you to access Predix Analytics Catalog features via the CLI.

Using this plugin, you can upload and test your Predix analytics services. For instance, one can add “an analytic” to the catalog, delete it, test it in a Cloud Foundry environment, and download an analytic’s artifact through the CF CLI.

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Using Spark Streaming, Apache Kafka, and Object Storage for Stream Processing on Bluemix

Ilya Drabenia

stream-processing-apache-spark-kafka-on-ibm-bluemix

One of the key points in the Industrial Internet is stream data processing. Equipment fault monitoring, predictive maintenance, or real-time diagnostics are only a few of the possible use cases. Some of the services provided by IBM Bluemix enable you to significantly speed up the implementation of such use cases. With Bluemix, you are not required to deploy and configure Hadoop, Apache Kafka, or other big data tools. It allows you to launch service instances in a few clicks.

In this article, we explain how to integrate and use the most popular open-source tools for stream processing. We explore IBM Message Hub (for collecting streams), the Apache Spark service (for processing events), and IBM Object Storage (for storing results).

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Using Predix Analytics Services from a Node.js App

Nick Herman

ge-predix-analytics-services-v1

Predix Analytics is a set of services that enable developers to use analytics solutions implemented by data scientists from their own apps. In this post, we explain how to get started with the three services required for running Predix analytics: Analytics Catalog, Analytics Runtime, and Analytics User Interface.

With the provided instructions, you will be able to set up the services and call the Univariate Anomaly Detection analytic service from a Node.js application.

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Data Stream Processing on IBM Bluemix: Streaming Analytics, Apache Spark, and BigInsights

Ilya Drabenia

bluemix-data-streams-apache-spark-hadoop-ibm-streams

Essentially, the Internet of Things is about collecting and exchanging data, which then can be used in many different ways. Equipment fault monitoring, predictive maintenance, or real-time diagnostics are only a few of the possible scenarios. Dealing with all this information, however, creates certain challenges for the field of the IoT, and stream processing of huge amounts of data is among them.

In this article, we compare IBM Bluemix services for real-time processing of data streams, such as IBM InfoSphere Streams, a managed Apache Spark service, and IBM BigInsights for Apache Hadoop.

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