Mobodexter launches new Paasmer Edge Software Products in its Edge Marketplace

Mobodexter has released a new version 3.0 of the Paasmer Edge Software with Edge Rules Engine, Enhanced versions of Edge Analytics and Machine Learning and Edge Artifical Intelligence. With this new release with the new features, Mobodexter is launching 5 new products on its Edge Marketplace https://shop.mobodexter.com

 

This blog presents the new products with the details on what they offer to customers and monthly subscription details

 

Paasmer Edge free trial – $0

 

Paasmer Edge Software Free trial offers all the Edge and Cloud features for 15 days trial.

 

Paasmer Edge Fundamentals – $300

 

Paasmer Edge Fundamentals offers all the core edge features with Edge Rules Engine along with all the cloud features that include device shadow, device management, feed management, configurable data visualization, and remote actuator control.

 

Highlights

  • Offers Edge core with Edge Rules Engine
  • Edge rules engine empowers customers to automate control functions on the Edge based on various conditions
  • Easy to use UI for Rules creation and deployment
  • In-built support for IoT connectivity protocols including WiFi and Bluetooth.
  • Easy dongle connect and support for Gateway applications of up to 1000 sensors
  • Real-time processing with minimal latency
  • Paasmer Edge Artificial Intelligence feature that demonstrates Paasmer’s capability to build AI based Edge solutions using Tensor flow and OpenCV

 

Paasmer Edge Analytics – $400

 

PAASMER Edge Analytics saves you thousands of dollars by running analytics of the live data on EDGE before sending it to PAASMER cloud and execute. It offers to auto-execute control functions at Edge in real-time based on the analytics functions. All cloud features that include device shadow, device management, feed management, configurable data visualization, and remote actuator control.

Highlights

  • Combines Edge Core with Edge Rules Engine, Edge Analytics, and all cloud features
  • Analytics offered for Aggregate, Average and Feed Monitoring
  • An easy to use UI based configurations to select the input feeds to do selected analytics.
  • Auto-execute control functions based on the analytics function
  • Advanced cloud flexibility with cloud Rules Engine enabled for predefined conditions
  • Easy to use UI for Rules creation and deployment
  • In-built support for IoT connectivity protocols including WiFi and Bluetooth.
  • Easy dongle connect and support for Gateway applications of up to 1000 sensors.
  • Real-time processing with minimal latency
  • Paasmer Edge Artificial Intelligence feature that demonstrates Paasmer’s capability to build AI based Edge solutions using Tensor flow and OpenCV.
  • Features of Edge Analytics Offered
    • Aggregate – Calculates the minimum value, maximum value, mean and standard deviation on the last n numbers of feed values in the stream.
    • Average – Calculates the average of last n numbers of feed values
    • Feed Monitoring – Continuously monitors the change in the feed value and updates it in the Paasmer platform.

 

Paasmer Machine Learning – $500

 

PAASMER Machine Learning has a built-in ML layer on Edge core. The PAASMER cloud supports the developer to train and test with different algorithms for their data set to choose the best one with maximum accuracy and it offers all the cloud features that include device shadow, device management, feed management, configurable data visualization, and remote actuator control. At the edge, it offers to auto-execute control functions in real-time based on the ML output data.

 

Highlights

  • Combines Machine learning with Edge Core
  • Includes a developer kit to build a machine learning IoT application
  • Rich Data Visualization features and insights.
  • Train historic data for predictive analytics using Paasmer Cloud
  • Supports multiple Machine Learning algorithm including
    • KNN (k-nearest neighbor algorithm)
    • DT (Decision Tree)
    • LR (Logistic Regression)
    • SVM (Support Vector Machine)
    • RF (Random Forest)
    • K-Means
    • Linear Regression
    • Navies Bayes
  • An easy to use UI based configuration to select input feeds for a selected Machine learning model built using Paasmer ML framework and handle the predicted outputs from ML.
  • Auto-execute control functions based on the ML output data
  • Advanced cloud flexibility with Rules Engine enabled for predefined conditions
  • Easy to use UI for Rules creation and deployment
  • In-built support for IoT connectivity protocols including WiFi and Bluetooth.
  • Easy dongle connect and support for Gateway applications of up to 1000 sensors.
  • Real-time processing with minimal latency
  • Paasmer Edge Artificial Intelligence feature that demonstrates Paasmer’s capability to build AI based Edge solutions using Tensor flow and OpenCV.

 

Paasmer Edge Power – $600

 

PAASMER Edge Power is an all in one PAASMER Edge Software Suit with the highest level of built-in intelligence with Analytics, Machine learning and auto-execution of control functions at Edge with all the cloud features that include device shadow, device management, feed management, configurable data visualization, and remote actuator control.

 

Highlights

  • Combines capabilities of Machine Learning to Edge-Core with Edge Rules Engine & Edge Analytics
  • An easy to use UI for rules Creation and deployment for execution at Edge in real time
    Additional data support for voice and videos
  • Wide options for control automation at edge using sensor data, analytics and machine learning
  • Rich set of troubleshooting capabilities with remote log access and support
  • Advanced cloud flexibility with Rules Engine enabled for predefined conditions
  • In-built support for IoT connectivity protocols including WiFi and Bluetooth.
  • Easy dongle connect and support for Gateway applications of up to 1000 sensors.
  • Real-time processing with minimal latency
  • Paasmer Edge Artificial Intelligence feature that demonstrates Paasmer’s capability to build AI based Edge solutions using Tensor flow and OpenCV.

 

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Paasmer Edge Software 3.0 and its new features

Mobodexter has released the version 3.0 of the Paasmer Edge Software that offers a new Edge feature “Edge Rules Engine” with the enhanced versions of Edge Analytics and Machine learning along with the basic version of Paasmer Edge Artificial Intelligence.

 

Paasmer Edge Analytics Enhanced

 

The enhanced version of Edge Analytics offers customers, an easy to use UI based configurations to select the input feeds to do selected analytics. Customers can now execute control functions based on the analytics function.

 

Paasmer Machine Learning Enhanced

 

The enhanced version of Machine Learning (ML) offers additional algorithms to train Machine learning models that will help to cover more use cases to build ML application and it also offers an easy to use UI based configuration to select input feeds for a selected Machine learning model built using Paasmer ML framework and handle the predicted outputs from ML. Customers can now execute control functions based on the ML output data

 

Paasmer Edge Rules Engine

 

The new Edge rules engine feature can empower customers to automate control functions on the Edge based on the precondition performed on various types data that includes sensor data, EA output data and ML output data.

 

With the enhanced version of the Edge Analytics and the Machine Learning, rules can be created with the data from analytic functions executed on Edge, the data from machine learning and various combinations for the sensor, analytics, and machine learning data to run automated control functions on the Edge.

 

The easy to use drag and drop based UI helps customers to create rules for each device with selected feeds, supported services, and set condition. Once all the rules are created, the deploy interface on UI helps to deploy the rules on the Device. The rules will be executed after the successful deployment

 

Paasmer Edge Artificial Intelligence

 

Paasmer Edge has the capability to run Artifical intelligence at Edge. The basic version of this feature released as part of Paasmser 3.0 demonstrates the Paasmer’s capability to build AI based Edge solutions using OpenCV. This feature that includes object deduction use case is available for free download from Paasmer development portal.

 

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PAASMER Edge Software Releases Version 2.1

New Updates Include: Machine Learning Developer Kit, Easier Device Management and New Ways to Configure Your Data

Paasmer has been consistently upgrading their software stack over the past couple of years based on feedback from their IoT developers and clients. Last year, Paasmer extended its reach and support beyond IoT into drone, robotics, and smart city applications. Earlier this year Paasmer made major updates to the  Edge Analytics; Edge Machine Learning & Edge Artificial Intelligence software packages with the Paasmer 2.0 release.


Today, Paasmer 2.1 launched with an upgraded Machine Learning Developer Kit and enhanced Paasmer Cloud GUI. THese new enhancements make it easier for developers to build machine-learning applications on the Edge. Typically,  historical data sets and related parameters are used to train machine-learning applications; however, the new trained module will be deployed on the device itself which allows for continuous training and improved accuracy.

Data visualization has also been updated in Paasmer Cloud GUI the new update allows developers to choose a different data configuration and presentation for each feed. The following presentation types have been added with the Paasmer 2.1  release: line chart, bar chart, circular gauge, vertical gauge, button and slider bar button.

The biggest enhancement made to last years Paasmer 2.0 version was the addition of dockerized architecture on their Edge Software. To accompany the dockerized architecture, Paasmer 2.1 features a new section in the left side panel of the Cloud GUI called, “Docker Device”. This new section makes it easier for developers to replace a broken or malfunctioning device without having to buy a new license for faster prototyping and more tests on multiple devices at a time.

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Key Drivers of Business Intelligence & Analytics market

ManagementTeamMouli1

Chandramouli Srinivasan

The overall BI and analytics market segment continues to expand and is expected to sustain its 2014 growth rate of 5.8% (adjusted for constant currency) through 2019 — as reflected in Gartner’s current estimate of the compound annual growth rate for the sector. However, this lower rate of growth reflects a market in transition, with changing buying patterns and requirements. Purchasing decisions continue to move from IT leaders to line-of-business executives and users who want more agility and more flexible personalized options — making the land-and-expand model the new norm. This is in stark contrast to the large, enterprise-scale deals that fueled double-digit growth at a time when IT had larger budgets and wielded much more influence in buying decisions.

The primary drivers of new growth in this rapidly evolving market are being influenced by the following dynamics:

  • New vendors continue to emerge, offering innovative products to the market for buyers to consider. During the next several years, buyers will benefit from the attention that vendors are giving to the BI and analytics market and will have ample opportunity to invest in the innovative product offerings that are brought to market. The downside of having a plethora of innovative products to pilot and vendors to engage in POCs with, is the tendency for organizations to incur technical debt over time — as multiple stand-alone solutions that demonstrate business value quickly (and hastily) turn into production deployments without adequate attention being paid to design, implementation and support.
  • The increased need for governance will serve as the catalyst for renewed IT engagement as business-user-led deployments expand. When the market shift first began, business users felt empowered to circumvent IT and autonomously purchase and begin using any product that addressed the gaps in their enterprise BI program.
  • Market awareness and adoption of smart data discovery will extend data discovery to a wider range of users, increasing the reach and impact of analytics. These emerging capabilities facilitate discovery of hidden patterns in large, complex and increasingly multi-structured datasets, without building models or writing algorithms or queries.
  • The need for organizations to integrate and derive insight from a growing number of multi-structured data sources will drive innovation in smart self-service data preparation and smart data discovery. Organizations will require sophisticated software capabilities that automate the ingestion, inference, enrichment and creation of search indexes when accessing new data sources.
  • Search-based data discovery enabled by natural-language query will extend the reach of analytics to more users. As BI and analytics platforms increasingly support natural-language query, allowing nontechnical users to analyze data by asking questions in a conversational way, new users are more likely to engage with and leverage analytics.
  • Marketplaces will expand and mature, creating new opportunities for organizations to buy and sell analytic capabilities. The availability of an active marketplace where buyers and sellers converge to exchange analytic applications, aggregated data sources, custom visualizations and algorithms is likely to generate increased interest in the BI and analytics space and to fuel its future growth.
  • Organizations will need to support real-time events and streaming data capture in support of IoT use cases. In order for organizations to prepare for the volume of data that is generated by devices, sensors and people in a connected world, organizations will have to make new investments in products that are designed to capture and process this type of data.

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Cloud Services Opportunity

Cloud Services Opportunity – Prime Time for Cloud Service Players to different and win the market place

ManagementTeamMouli1

Chandramouli Srinivasan

I was talking to an analyst about cloud services business opportunity for SMB Players like MoboDexter. A few key points emerged that were really interesting and concurred with our understanding as MoboDexter’s Cloud Services Offering is already into this space and working with players like Harman Connected Services. Sharing the key points from this discussion here.
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