#Amazon SP API Development

[inventorymanagementsystem]

In the intricate landscape of Amazon Selling Partner API Development, mastering the authentication process is your golden key to a world of seamless integration and enhanced e-commerce capabilities.

[intersoft4u]

The Developer Partner You Can Trust for Amazon SP-API

As the e-commerce landscape continues to evolve, Inter-Soft reaffirms its commitment as a leading technology solutions provider with a specialised focus on Amazon's Seller Partner API (SP-API). With a dedicated team of experts, Inter-Soft proudly announces its status as a trusted Amazon SP-API developer partner, solidifying its position as an industry frontrunner in delivering innovative solutions for Amazon sellers.

Amazon, a global e-commerce giant, has been at the forefront of empowering businesses to thrive in the digital marketplace. With the introduction of SP-API, Amazon has provided developers with enhanced functionalities to create powerful tools that enable sellers to manage their businesses more efficiently.

Inter-Soft recognises the importance of SP-API in revolutionising the seller experience on Amazon. Leveraging our in-depth understanding and expertise in SP-API development, we are committed to delivering cutting-edge solutions that streamline processes, optimise workflows, and empower sellers to achieve their business goals seamlessly.

As an Amazon developer and SP-API specialist, Inter-Soft offers a comprehensive suite of services tailored to meet the diverse needs of Amazon sellers. From API integration and custom software development to SP-API troubleshooting and support, our team works tirelessly to ensure that sellers harness the full potential of Amazon's platform.

"We are thrilled to be recognised as a trusted Amazon SP-API developer partner," Said a spokesperson at Inter-Soft. "Our goal is to empower Amazon sellers with robust solutions that simplify operations and drive growth. With our expertise in SP-API development, we aim to be the catalyst for success in the ever-evolving e-commerce landscape."

At Inter-Soft, we prioritise collaboration, innovation, and client satisfaction. Our commitment to excellence and dedication to staying abreast of the latest advancements in SP-API technology set us apart as a reliable partner for Amazon sellers worldwide.

For more information about Inter-Soft's Amazon SP-API development services and how we can help elevate your Amazon selling experience, please visit our website at https://inter-soft.com/.

Inter-Soft is a leading technology solutions provider specialising in Amazon SP-API development. With a team of seasoned experts, Inter-Soft offers innovative solutions tailored to enhance the Amazon selling experience for businesses globally.

[ecommercedeveloper24]

What Is The Amazon Selling Partner Api?

The Amazon Selling Partner API is a collection of APIs that enable organizations to connect to Amazon’s selling platform. It provides real-time access to data. So that you can use it to build new apps, automate operations, and improve existing workflows. The SP API is intended to replace Amazon’s existing Marketplace Web Service (MWS) API more efficiently.

The Amazon Selling Partner API includes several APIs that offer different functionalities:

Vendor Retail Analytics API:This gives you access to information regarding a vendor’s sales performance, such as sales and inventory data.

Brand Analytics API: Provides Amazon retailers and sellers with information about their customers’ purchasing habits, such as search phrases and purchase behavior.

Seller Retail Analytics API:Provides Amazon sellers with insights into their performance, including sales and inventory data.

Seller Inventory API:Allows Amazon sellers to manage their inventory more efficiently and avoid stockouts.

Can my business access the SP-API?

Benefits of Selling Partner API

The Amazon Selling Partner API provides several benefits to businesses, including:

Real-time access to data: The API gives real-time data access, allowing firms to react swiftly to changes in sales performance and inventory levels.

Improved automation: Businesses can reduce labor costs and potential errors by automating processes such as inventory management, order fulfillment, and pricing adjustments.

Enhanced customer experience:Businesses can better understand their customers’ wants and preferences by acquiring data about their shopping behaviour and tailoring their services accordingly.

How to Start Using Selling Partner API?

To start using the Amazon Selling Partner API, businesses must first register as Amazon sellers or vendors and meet the eligibility requirements. They must also create an Amazon developer account and obtain API credentials. Once they have their credentials, they can use the APIs to access data and functionality from Amazon’s selling platform.

Continue Reading…

[viswatechynology]

Android

Android

Android is an open source and Linux-based Operating System for mobile devices such as smartphones and tablet computers. Android was developed by the Open Handset Alliance, led by Google, and other companies.

Android offers a unified approach to application development for mobile devices which means developers need only develop for Android, and their applications should be able to run on different devices powered by Android.

The first beta version of the Android Software Development Kit (SDK) was released by Google in 2007 where as the first commercial version, Android 1.0, was released in September 2008.

Read More

On June 27, 2012, at the Google I/O conference, Google announced the next Android version, 4.1 Jelly Bean. Jelly Bean is an incremental update, with the primary aim of improving the user interface, both in terms of functionality and performance.

The source code for Android is available under free and open source software licenses. Google publishes most of the code under the Apache License version 2.0 and the rest, Linux kernel changes, under the GNU General Public License version 2.

Features of Android

Android is a powerful operating system competing with Apple 4GS and supports great features. Few of them are listed below −

Feature & Description

Read More

Beautiful UI

Android OS basic screen provides a beautiful and intuitive user interface.

Connectivity

GSM/EDGE, IDEN, CDMA, EV-DO, UMTS, Bluetooth, Wi-Fi, LTE, NFC and WiMAX.

Storage

SQLite, a lightweight relational database, is used for data storage purposes.

Media support

H.263, H.264, MPEG-4 SP, AMR, AMR-WB, AAC, HE-AAC, AAC 5.1, MP3, MIDI, Ogg Vorbis, WAV, JPEG, PNG, GIF, and BMP.

Messaging

SMS and MMS

Web browser

Based on the open-source WebKit layout engine, coupled with Chrome’s V8 JavaScript engine supporting HTML5 and CSS3.

Multi-touch

Android has native support for multi-touch which was initially made available in handsets such as the HTC Hero.

Multi-tasking

User can jump from one task to another and same time various application can run simultaneously.

Resizable widgets

Widgets are resizable, so users can expand them to show more content or shrink them to save space.

Multi-Language

Supports single direction and bi-directional text.

GCM

Google Cloud Messaging (GCM) is a service that lets developers send short message data to their users on Android devices, without needing a proprietary sync solution.

Wi-Fi Direct

A technology that lets apps discover and pair directly, over a high-bandwidth peer-to-peer connection.

Android Beam

A popular NFC-based technology that lets users instantly share, just by touching two NFC-enabled phones together.

Android Applications

Android applications are usually developed in the Java language using the Android Software Development Kit.

Once developed, Android applications can be packaged easily and sold out either through a store such as Google Play, SlideME, Opera Mobile Store, Mobango, F-droid and the Amazon Appstore.

Android powers hundreds of millions of mobile devices in more than 190 countries around the world. It’s the largest installed base of any mobile platform and growing fast. Every day more than 1 million new Android devices are activated worldwide.

This tutorial has been written with an aim to teach you how to develop and package Android application. We will start from environment setup for Android application programming and then drill down to look into various aspects of Android applications.

Categories of Android applications

There are many android applications in the market. The top categories are –

· Music

· Sports

· Travel

· Business

· Social Media

· NEWS

· Life style

· Weather

· References

· Uitilities

· Multi Media

· Food & Drink

· Books

· Neavgation

· Finance

What is API level versions ?

Read More

API Level is an integer value that uniquely identifies the framework API revision offered by a version of the Android platform.

Platform Version

Android 6.0

Android 5.1

Android 5.0

Android 4.4W

Android 4.4

Android 4.3

Android 4.2, 4.2.2

Android 4.1, 4.1.1

Android 4.0.3, 4.0.4

Android 4.0, 4.0.1, 4.0.2

Android 3.2

Android 3.1.x

Android 3.0.x

Android 2.3.4

Android 2.3.3

Android 2.3.2

Android 2.3.1

Android 2.3

Android 2.2.x

Android 2.1.x

Android 2.0.1

Android 2.0

Android 1.6

Android 1.5

Android 1.1

Android 1.0

[viswatech]

Android?

Android is an open source and Linux-based Operating System for mobile devices such as smartphones and tablet computers. Android was developed by the Open Handset Alliance, led by Google, and other companies.

Android offers a unified approach to application development for mobile devices which means developers need only develop for Android, and their applications should be able to run on different devices powered by Android.

The first beta version of the Android Software Development Kit (SDK) was released by Google in 2007 where as the first commercial version, Android 1.0, was released in September 2008.

Read More

On June 27, 2012, at the Google I/O conference, Google announced the next Android version, 4.1 Jelly Bean. Jelly Bean is an incremental update, with the primary aim of improving the user interface, both in terms of functionality and performance.

The source code for Android is available under free and open source software licenses. Google publishes most of the code under the Apache License version 2.0 and the rest, Linux kernel changes, under the GNU General Public License version 2.

Features of Android

Android is a powerful operating system competing with Apple 4GS and supports great features. Few of them are listed below −

Feature & Description

Read More

Beautiful UI

Android OS basic screen provides a beautiful  and intuitive user interface.

Connectivity

GSM/EDGE, IDEN, CDMA, EV-DO, UMTS, Bluetooth,  Wi-Fi, LTE, NFC and WiMAX.

Storage

SQLite, a lightweight relational database, is  used for data storage purposes.

Media support

H.263, H.264, MPEG-4 SP, AMR, AMR-WB, AAC,  HE-AAC, AAC 5.1, MP3, MIDI, Ogg Vorbis, WAV, JPEG, PNG, GIF, and BMP.

Messaging

SMS and MMS

Web browser

Based on the open-source WebKit layout engine,  coupled with Chrome's V8 JavaScript engine supporting HTML5 and CSS3.

Multi-touch

Android has native support for multi-touch  which was initially made available in handsets such as the HTC Hero.

Multi-tasking

User can jump from one task to another and  same time various application can run simultaneously.

Resizable widgets

Widgets are resizable, so users can expand  them to show more content or shrink them to save space.

Multi-Language

Supports single direction and bi-directional  text.

GCM

Google Cloud Messaging (GCM) is a service that  lets developers send short message data to their users on Android devices,  without needing a proprietary sync solution.

Wi-Fi Direct

A technology that lets apps discover and pair  directly, over a high-bandwidth peer-to-peer connection.

Android Beam

A popular NFC-based technology that lets users  instantly share, just by touching two NFC-enabled phones together.

Android Applications

Android applications are usually developed in the Java language using the Android Software Development Kit.

Once developed, Android applications can be packaged easily and sold out either through a store such as Google Play, SlideME, Opera Mobile Store, Mobango, F-droid and the Amazon Appstore.

Android powers hundreds of millions of mobile devices in more than 190 countries around the world. It's the largest installed base of any mobile platform and growing fast. Every day more than 1 million new Android devices are activated worldwide.

This tutorial has been written with an aim to teach you how to develop and package Android application. We will start from environment setup for Android application programming and then drill down to look into various aspects of Android applications.

Categories of Android applications

There are many android applications in the market. The top categories are –

·         Music

·         Sports

·         Travel

·         Business

·         Social Media

·         NEWS  

·         Life style  

·         Weather

·         References

·         Uitilities

·         Multi Media

·         Food & Drink  

·         Books

·         Neavgation

·         Finance

 What is API level versions ?

Read More

API Level is an integer value that uniquely identifies the framework API revision offered by a version of the Android platform.

Platform Version                             

Android 6.0

Android 5.1

Android 5.0

Android 4.4W

Android 4.4

Android 4.3

Android 4.2, 4.2.2

Android 4.1, 4.1.1

Android 4.0.3, 4.0.4

Android 4.0, 4.0.1, 4.0.2

Android 3.2

Android 3.1.x

Android 3.0.x

Android 2.3.4

Android 2.3.3

Android 2.3.2

Android 2.3.1

Android 2.3

Android 2.2.x

Android 2.1.x

Android 2.0.1

Android 2.0

Android 1.6

Android 1.5

Android 1.1

Android 1.0

[nishijain777]

Virtual reality Application | Application development Services| Augmented Reality

Virtual reality Application | Application development Services| Augmented Reality

Labels shape our perception of the world. We usually prefer to know the names of objects, people, and places we interact with or even more - what brand of a particular product we are going to purchase reference and the response of others give about quality. The device is equipped with image recognition can automatically detect the labels. An image recognition application software for smartphones is just a tool to capture and detect the name of digital photos and video.

Also, Read: virtual reality Application Development  

By developing highly accurate, controllable, and flexible image recognition algorithms, it is now possible to identify the images, text, video, and objects. Let's find out what it is, how it works, how to create an image recognition application, and what technology is used when doing so.

What image recognition in artificial intelligence?

AI-based systems have also begun to computers outperform trained on a less detailed knowledge of the subject.

AI image recognition is often considered a single term is discussed in the context of computer vision, machine learning as part of artificial intelligence, and signal processing. To put it in a nutshell, the recognition of a particular image of three. So, basically, the image recognition software may not be used synonymously for signal processing, but can definitely be considered as part of a large domain of AI and computer vision. Let's take a closer look at what each of the four concepts mean.

image recognition in Artificial Intelligence

Also, Read virtual reality Application Development

image recognition. With the image into the main input and output elements, image recognition is designed to understand the visual representation of a particular image. In other words, the software is trained to extract a lot of useful information and perform an important role to provide answers to questions such as the picture. This is how the term recognition image is usually understood.

signal processing. can input not only images but also a variety of signals such as voice and biological measurements. This is a useful signal when it comes to speech recognition as well as for a variety of applications such as face detection. SP is a wider field than the image identification technology and mixed with profound learning, it is able to discover patterns and relationships that, until now, were not observed.

computer vision. This is a whole related disciplines by building artificial systems that receive information from input sources such as images, video, or data hyperspectral more multi-dimensional. The process involves a computer vision techniques such as face detection, segmentation, tracking, pose estimation, localization and mapping, and object recognition. These data are processed by the application programming interface (API), which will be discussed later in the article.

Also, Read: virtual reality Application Development  

Machine learning. This is a general term for all of the above concepts. ML includes image recognition, signal processing, and computer vision. Moreover, it is quite a common framework in terms of input and output - it takes any sign of input return information quantitatively or qualitatively, signals, images or video as output. This diversity of requests and responses is enabled through the use of a large and complex ensemble of general machine learning algorithms.

How the image recognition software work

Image detection is done by using two different methods. This method is referred to as a neural network method.

In supervised learning, the process used to determine whether a particular image in a particular category, and then compared with those in the categories that have been detected. In unsupervised learning, the process used to determine whether the images in a category by itself. complex neural network computational methods designed to enable the classification and tracking of images.

Also, Read: virtual reality Application Development  

What you should know is that the image recognition application software most likely will use a combination of supervised and unsupervised algorithms.

Classification method (also called supervised learning) using machine-learning algorithms to estimate the features in the picture called essential characteristics. It then uses this feature to make predictions about whether an image may be of interest to a particular user. A machine learning algorithm will be able to tell whether an image contains an important feature for the user.

Metadata classify the images and extract information such as size, color, format, and the format of the border. Figure categorized in different tags, called class information, and each tag associated with the image. This information is used by the class recognition engines to understand the "meaning" of the image.

The data used to identify the image, for example: "cute baby" or "pictures of dogs", should be labeled to be useful. This requires the data to be analyzed using information extraction techniques such as classification or translation.

Thus, the pattern recognition in image processing is a multi-step process that includes:

Detection of the original image

Analysis and classification of data

reinforcement learning

AI training process

Monitoring and twisting of the training process

Also, Read: virtual reality Application Development  

How to choose an image recognition API?

Another important component to keep in mind when aiming to create an image recognition application is API. Various APIs computer vision has been developed since the beginning of AI and ML revolution. Image recognition API to take advantage of the latest technological advances and provide recognition applications your photo image matching the power to offer better and more powerful features. Thus, the service hosts available APIs to integrate with existing applications or used to build a particular feature or an entire business.

Also Read: Virtual reality App Developers  

Not every company has sufficient resources to invest in building out the entire engineering team of computer vision. So, the following is a list of image recognition API that you need to pay attention to if you want some solutions off-the-shelf open source to make your life easier:

API Google Cloud Vision. Google Cloud Vision API allows you to upload images or create custom datasets for image recognition. It helps you look for patterns of known human and produce an image of them. It is available on the Google Cloud Platform (GCP). You can integrate it with some image processing projects, as well as in your own application.

Amazon Rekognition. One of the best ways to perform image recognition is to use Amazon's system. Amazon Rekognition offers diversity API that allows you to train your own visual recognition engine and image segmentation & Video detect and analyze objects, faces, or explicit content, recognize faces or the faces of celebrities and much more.

 Also Read: Virtual reality App Developers  

IBM Watson Visual Recognition. Watson Visual Recognition of services on the IBM Cloud is suitable for many applications because it allows users to have flexibility in the use of the API. pre-trained models provided by the Visual Recognition service can be used to build applications that have the potential to perform in many settings. The model is then trained to detect certain classes of objects.

 Also Read: Virtual reality App Developers  

API Microsoft Computer Vision. This image recognition software is an integral part of the Cognitive Azure Services. This makes it possible to identify and analyze the content in the image. Additionally, use it, you can try to train the computer vision you to recognize the faces and emotions of society. It is easy to introduce Computer Vision services to your application - just add an API call.

 Also Read: Virtual reality App Developers  

API Clarifai. It is one of the best image search services. Community offers (with a free API key), Essential, and Enterprise plan to choose from. One can use either off-the-shelf image recognition models or build a model of their own custom trained. A ready-made model can detect faces, colors, clothing, identify foods, and other things. It is significantly faster than other search engines because it uses inference rather than directly finding.

[craigbrownphd-blog-blog]

Getting Started with Microsoft Azure – by the SAP HANA Academy

Introduction   Recently, we have recorded a number of new videos for the SAP HANA Academy to help you get started with SAP HANA on the Microsoft Azure public cloud environment. The full playlist can be accessed here: * Getting Started with SAP HANA on Microsoft Azure   This blog will be part of a series: * Getting Started with Microsoft Azure (this blog)   In this blog, I will provide some references and background information for the first four videos in the series.   SAP HANA Academy – Getting Started with SAP HANA on Azure (YouTube Playlist)   Marketplaces All cloud providers offer some sort of marketplace with solutions, that is, a virtual machine template with software pre-installed and post-configured. Typically, these solutions will save you a lot of time. For cloud-based hosting of SAP HANA, marketplace solutions are available for * Google Cloud Platform (GCP: see Getting Started with Google Cloud Platform) * Amazon AWS * Microsoft Azure SAP also provides such a marketplace and it’s called the SAP Cloud Appliance Library (CAL). Using CAL is very easy. Last year, I posted a blog about Running SAP HANA Express in CAL where this is demonstrated.  At the time, SAP HANA, express edition was just released and I used AWS as the cloud provider. Today, we can also – and just as easily – use Google Cloud, or Microsoft Azure as providers. Support for Azure dates back to 2014. Support for GCP was added just recently. Obviously, you are not limited to running a 32 GB express edition in the cloud. For example, on Azure, Microsoft offers units that have up to 960 CPUs and 20 TB memory – see SAP HANA (Large Instances) overview and architecture on Azure. Before we get into that, let’s first focus on the SAP HANA, express edition solution from CAL. As each cloud provider implements virtual machine deployment, network security groups (firewall), and disk provisioning slightly different, this also adds a bit of complexity if you have to manage and work in all these cloud provider environments. SAP CAL takes care of this complexity and makes all the necessary API calls to provision your HANA system as both required and desired. For more information, see * SAP on Azure * SAP HANA on Azure * SAP HANA, express edition – SAP Developer Center * SAP Cloud Appliance Library   1. Create Azure Account in CAL In the first video, we create a CAL account for Microsoft Azure as a cloud provider. Again, very easy. All you need to do is link your Azure subscription using a Wizard. Takes a minute, if that. Microsoft currently offers free Azure trial accounts (with a $200 credit) * Create a free Azure account   Microsoft Azure > Subscriptions SAP Cloud Appliance Library > Accounts     Video Tutorial: Getting Started with SAP HANA on Azure: Create Account [2.0 SPS 02] URL: https://www.youtube.com/watch?v=x_lubRiGKiw   2. Create Instance In next video tutorial, we show you how you can create an instance of the SAP HANA, express edition solution in CAL with Microsoft Azure as the cloud provider account. Initially, this has very little to do with Azure as the steps will be exactly the same when using the Google Cloud Platform or AWS. However, once the instance has been created, we switch to the Microsoft Azure Dashboard to view what resources have been added as a result and how they can be configured. In particular, we will focus on the Network Security Group (Access Points in CAL), which defines the firewall configuration for our instance.   Microsoft Azure > All resources   Microsoft Azure > Network Security Group   Video Tutorial: Getting Started with SAP HANA on Azure: Create Instance [2.0 SPS 02] URL: https://www.youtube.com/watch?v=_XcxWleXypw   3. Access Points (Firewall)   In the third video, we zoom in on those access points and discuss the security aspects and default port assignment in SAP HANA with the express edition as an example but it would be similar for any other SAP HANA edition. Some of the access points discussed include * TCP 22 for SSH (Windows PowerShell) * TCP 1128/29 for SAP host agent * TCP 313/14 for the SAP Start Service * TCP 330 for SAP HANA XS Advanced model * TCP 43/80 for SAP HANA XS Classic model   The great number of ports opened for SAP HANA XS may come as an (unpleasant) surprise. Using hostname-based routing, only a single port would be opened so this will depend upon how XS is setup. For more information about this topic, try our not-so-serious * XS Exam Quiz – by the SAP HANA Academy   PowerShell SSH connection SAP Host Agent connection SAP Start Service connection XSA Controller connection CAL > Access Points for SAP HANA, express edition   Video Tutorial: Getting Started with SAP HANA on Azure: CAL Access Points [2.0 SPS 02] URL: https://www.youtube.com/watch?v=jpKVDP3ldgU   4. SAP HANA, express edition on Azure Marketplace Using SAP’s CAL to create an instance is easy because a lot of configuration options (read complexity) have been left out. However, there may be times when you need to do some fine-tuning. For this, you would have to switch to your Azure portal. Also, using CAL is not for free and maybe you are fluent in Azure and have no business in AWS or GCP.  In this case, using the solutions on the Azure Marketplace directly will make more sense. For the fine print about using CAL, see the FAQ on * SAP Cloud Appliance Library Community   In this fourth video, we create – once again – an SAP HANA, express edition instance, this time using the solution in the Microsoft Azure Marketplace. It is not exactly rocket science, yet. In fact, it is a 4-step wizard approach (this is Microsoft, after all) * Provide the basics: solution (VM) name, username, password (or key), and resource group * Accept the recommended virtual machine size (or adjust as desired) * Configure optional features (or not) * Agree with terms of use (if you agree, of course) However, at each step, you will have a range of options to fine-tune your instance, providing much more control as compared to using SAP CAL.   For the step-by-step tutorial by Lucia Subatin on SAP Developer Community, see * SAP HANA, express edition – Getting started with the Marketplace on Microsoft Azure   Microsoft Azure Marketplace > SAP HANA, express edition Microsoft Azure Marketplace > SAP HANA, express edition > Step 2. Size   Video Tutorial: Getting Started with SAP HANA on Azure: Azure Marketplace [2.0 SPS 02] URL: https://www.youtube.com/watch?v=2NwQNw6gOps   References For more information see: SAP HANA Academy Playlists * SAP HANA Express * Getting Started with SAP HANA on Microsoft Azure SAP * SAP Cloud Appliance Library * SAP Cloud Appliance Library Community * SAP HANA, express edition – SAP Developer Center Microsoft * SAP on Azure * SAP HANA on Azure * Create a free Azure account * Microsoft Azure | Dashboard * SAP HANA, express edition (Server + Applications) solution for the Microsoft Azure Marketplace * SAP HANA (Large Instances) overview and architecture on Azure SAP Blogs * SAP on Microsoft Azure – SCN Wiki * Running SAP HANA Express in CAL – by the SAP HANA Academy SAP Developer Tutorials * SAP HANA, express edition – Getting started with the Marketplace on Microsoft Azure SAP Notes * 1380654 – SAP support in public cloud environments * 2015553 – SAP on Microsoft Azure: Support prerequisites * 2316233 – SAP HANA on Microsoft Azure (Large Instances)   Thank you for watching The SAP HANA Academy provides technical enablement, implementation and adoption support for customers and partners with 1000’s of free tutorial videos. For the full library, see SAP HANA Academy Library – by the SAP HANA Academy For the full list of blogs, see Blog Posts – by the SAP HANA Academy * Subscribe to our YouTube channel for updates * Join us on LinkedIn linkedin.com/in/saphanaacademy * Follow us on Twitter @saphanaacademy * Google+ plus.google.com/+saphanaacademy * Facebook facebook.com/saphanaacademy   http://bit.ly/2CeKXLy #SAP #SAPCloud #AI

[trebarobota]

Senior Reliability Engineer/Senior DevOps

Senior Reliability Engineer/Senior DevOps

SPS Commerce (Ukraine) Required skills: What experience and skills do I need?— 3 or more years of experience in the Information Technology field Experience in:— Administering Linux — Oracle Enterprise Linux, Red Hat, CentOS— Participating in Agile Development Methodology and task execution— Amazon Web Services including Lambda, SNS, API Gateway, EC2, RDS, Dynamo DB, Route53, Elastic Load…

[joeyrob1]

Microsoft Azure Machine Learning Review

Microsoft Azure Machine Learning Review

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Microsoft Azure Machine Learning Review. Hot on the heels of our Amazon Machine Learning Review, we decided to do a review and compare against Microsoft Azure’s offering of Machine Learning services on the cloud. In short, we find Microsoft Azure Machine Learning services quite amazing and liked it better than Amazon’s Machine Learning services.

Free Trial Access

Nothing beats free trial access to test drive the system the system for free. Woo hoo! Free $200 credits remaining. I am beginning to sing “…This used to be my playground. This used to be my childhood dreams…”

Production Pricing

But we have to get real and check the real production pricing which can be found at Azure Machine Learning Pricing and copied below. The pricing model is unique. Base fee is low and hourly experiment fee is also low. I guess the bulk of the cost would show up if one publishes the API for production use and there is substantial transactional load on those API calls.

Azure Marketplace vs Machine Learning Studio

First thing I had to realize was how rich the Azure Machine Learning offerings were. There are pre-packaged working AI models available in the Azure MarketPlace For Machine Learning and there is Azure Machine Learning Studio where one can configure Machine Learning models using graphical interfaces. These are very different toolsets for different use cases.

Azure Marketplace

For example Azure MarketPlace has  packaged services like the following, some of which might have been built using Azure Machine Learning Studio and many many more.

Customer Churn Prediction

Customer Churn Prediction is a churn analytics service built with Azure Machine Learning. It’s designed to predict the likelyhood of a customer (player, subscriber, user, etc.) ending his or her relationship with a company or service.

Text Analytics

Text Analytics API is a suite of text analytics services built with Azure Machine Learning. Just bring your unstructured text (English only), and use this API to perform sentiment analysis and key phrase extraction.

Recommendations

Recommendations API by Azure Machine Learning helps your customer discover items in your catalog. Customer activity on your website is used to recommend items and to improve conversion in your digital or physical store.

Frequently Bought Together

Frequently Bought Together is a market basket analysis API built with Azure Machine Learning. It helps your customers discover items in your catalog that are frequently purchased together. Use your purchase history to add recommendations to your website.

Binary Classifier API

Binary Classifier API is an example built with Microsoft Azure Machine Learning that fits a logistic regression model to user inputted data and outputs the predicted value for each of the observations in the data. Suppose you have a dataset and would like to predict a binary dependent variable based on the independent variables. ‘Logistic Regression’ is a popular statistical technique used for such predictions.

You can subscribe to a service in the marketplace and try to use it. For example, here we try the text analysis service.

It was very simple to set up. However, I was wondering why the supported languages didn’t include usual internet languages like Python and Ruby.

Experiment with Text Analytics

We went ahead and tried exploring the text analytics API in the browser without code.

We took some text from a Marketwatch article:

http://www.marketwatch.com/story/sp-pullback-may-be-investors-last-chance-2015-04-13

1.

Text 1: As much as the market rallied this past week, we still have not seen dissipation of the bearishness in the financial media. Most seem way too focused on corporate earnings, and believe that will be the next shoe to drop, and cause the market to drop.

Key phrases identified were: “bearishness”,”financial media”,”market”,”past week”,”corporate earnings”,”dissipation”,”shoe”

Sentiment score was: 0.287 (I believe where 0 is very negative sentiment is 1 is very positive sentiment)

2.

Text 2: The upcoming week should provide us with the answer to the question I posed in the title of this update. Keep in mind we have been looking at the last month as a consolidation setting us up for a strong rally toward 2200 in the S&P 500 and 132-136 in the IWM. So, if next week does not provide us with weakness in the equity market, and we take out the all-time highs, we are clearly heading much higher, and toward our long-time targets much sooner than later. But should the market be so kind as to provide us with one more pullback or even one more low toward 2021ES, it will likely be your last opportunity to enter for this next rally to 2182ES/2189SPX and 132-136 in the IWM.

Key phrases identified were:  “strong rally”,”weakness”,”long-time targets”,”title”,”equity market”,”us”,”question”,”time highs”,”consolidation”,”answer”,”kind”,”upcoming week”,”SPX”,”update”,”pullback”,”month”,”opportunity”,”mind”,”IWM”

Sentiment score was: 0.99 (I wonder why the score was so high?)

3.

Text 3: Oh no. The world is coming to a sad end.

Sentiment score was: 0.00 (Ok, this makes sense)

4.

Text 4: Oh yes. The world is a beautiful place.

Sentiment score was: 0.88 (This makes sense too)

5.

Text 5: Oh no. The world is a beautiful place.

Sentiment score was: 0.55 (Now, I am just fooling around and confusing everyone)

Overall, the Text Analytics service performed as I expected and is slightly better than other Text Analytics engines I have tried.

Azure Machine Learning Studio

Now, Azure Machine Learning Studio is what makes the whole experience rock. The graphical user interface is powerful and yet amazingly simple and intuitive to use. First you create a workspace like following

You can find plenty of documentations at:

http://azure.microsoft.com/en-us/documentation/services/machine-learning/

http://blogs.technet.com/b/machinelearning/

http://blogs.technet.com/b/machinelearning/archive/2015/04/09/exciting-new-templates-in-azure-ml.aspx

What is amazing is the array of samples and templates you can use for experiments eg binary classification, regression, credit risk anomaly detection, customer relationship prediction, flight delay prediction, prediction of student performance, twitter sentiment analysis, recognition of hand-writing, neural networks, online fraud detection, movie and production recommendations, time series forecasting etc.

You can see the full listing at the Machine Learning Gallery http://gallery.azureml.net/

Compared to the narrow set of possibilities at Amazon, this is where Azure really shines. Now I am wondering why I discovered Azure Machine Learning so late. Was it because I overlooked it in favour of open-source source libraries or typically open-source friendly vendors like Amazon and neglected to see how powerful Microsoft platforms can be?

Amazing UI in Azure Machine Learning Studio

The graphical UI in the Machine Learning Studio is wonderful to say the least. Feels like a sophisticated thick client but all in my Chrome Browser with animation, graphical flow charts to help you understand a complicated process.

I decided not to go easy on Azure and tried stress testing by running multiple concurrent model evaluations.

It does take quite a while to run some of the models. Filter Based Feature Selection was stuck for over 30 minutes for the Binary Classification of Twitter sentiment. The other experiments each only took about 5-10 minutes to complete which was acceptable.

The simple ability to share your results (eg on Twitter) is a nice friendly aspect great for their marketing too.

Conclusion

We have tried many machine learning platforms before from the powerful open-source ones like Python Sklearn, Mahout to open-source commercial ones like 0xData, Prediction.IO to large vendor on the cloud ones like Amazon Machine Learning and now Microsoft Azure.

In terms of being able to write code in however way you like and integrate easily into your code, Python Sklearn is probably still the most flexible but comes with a medium learning curve and only for developers. But if you want a Machine Learning service on the cloud for beginners to experts to even business users, with a powerful and intuitive user interface at a decent cost, I think Microsoft Azure Machine Learning Studio and Marketplace clearly outshines all other Machine Learning solutions on the cloud.

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RobustTechHouse is a leading tech company focusing on mobile app development in Singapore. If you are interested to engage RobustTechHouse on your projects, you can contact us here.

Microsoft Azure Machine Learning Review was originally published on RobustTechHouse - Mobile App Development Singapore

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In the ever-evolving landscape of e-commerce, businesses must stay agile and adapt to technological advancements. One such pivotal shift is the migration from Amazon's Marketplace Web Service (MWS) to the more modern and efficient Selling Partner API (SP-API).

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What is the Amazon Seller Central API? Can I access it?

As the world's largest online retailer, Amazon provides a platform for millions of sellers to reach customers around the world. To help sellers manage their accounts and listings, Amazon offers a suite of APIs, including the Amazon Seller Central API and the newer Amazon Selling Partner API (SP API). In this blog, we'll take a closer look at these APIs and how you can access them.

What is the Amazon Seller Central API?

The Amazon Seller Central API is a legacy set of APIs that allows sellers to access and manage their Amazon seller accounts programmatically. With the Amazon Seller Central API, sellers can perform a range of tasks, such as managing orders, inventory, and listings, retrieving reports, and more.

To use the Amazon Seller Central API, sellers need to be registered on Amazon and have a Professional Selling Plan. They also need to apply for access to the API through the Amazon Developer Services portal and follow Amazon's guidelines and requirements for using the API.

What is the Amazon Selling Partner API?

The Amazon Selling Partner API (SP API) is a more recent set of APIs that provide access to Amazon selling data, functionality, and tools. The SP API is designed to replace the Amazon Marketplace Web Service (MWS) and offers several advantages over the legacy API, including a more modern architecture, improved performance, and expanded functionality.

The Amazon Selling Partner API includes a range of modules, such as Orders, Reports, Fulfillment, and Catalog Items. These modules allow sellers to manage their orders, inventory,and listings, retrieve reports, and more, all through a single, unified API.

Continue Reading...

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The Amazon Selling Partner API (SP-API) is a powerful tool that helps third-party developers integrate with Amazon's massive ecommerce platform. With SP-API and Amazon Selling Partner API Development, developers can build applications and services that leverage Amazon's product data, order management system, and more.

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In the dynamic realm of e-commerce, staying at the forefront of innovation is paramount. Amazon SP-API development has emerged as a game-changer, empowering sellers with advanced tools and features. Let's embark on a journey through the latest enhancements, unlocking new possibilities for your e-commerce ventures.

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In the dynamic landscape of e-commerce, staying ahead requires tools that not only keep up with your business needs but also propel you towards innovation. Enter Amazon Selling Partner API (SP-API), a game-changing solution designed to transform your Amazon selling experience.

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Embark on a journey to elevate your online selling experience with the Amazon Selling Partner API—an indispensable toolkit for Amazon sellers seeking operational efficiency and accelerated growth. The Amazon Selling Partner API serves as a robust foundation for sellers, enabling seamless integration with the Amazon marketplace.