• Top 10 RPA Service Providers to Automate your Business Processes

      Adaptive service providers in Robotic Process Automation or RPA implementation domains are finding solutions to fill emerging service gaps in industries impacted by changes across the world at the macro-level. Traditionally, RPA is used extensively in manufacturing-intensive industries and healthcare industries. However, the newer world dynamics are driving immense adaptation and RPA use is evolving at a faster pace in services delivery as well.



      Mainly business functions like outsourcing, financial services and insurance, banking, procurement outsourcing, Human Resources Outsourcing which used RPA in a limited way are being further adapted to drive exponential growth in RPA services verticals by service providers with a vision to build newer paradigms for Automated services. RPA service providers India are setting new trends, as they develop customized solutions for every industry player.

      Analysts estimate that the Global Robotic Process Automation Market is poised to grow to $7.2 billion by 2025 at 32.6% CAGR (Research and Markets Report, 2020). Growth of CAGR for RPA in India is at 20% by 2025.

      In the newer world framework, organizations need to make strategic decisions and consciously adopt higher technology platforms like RPA for higher efficiency, performance and cost effective ROI. For local businesses, finding the best RPA service providers is a game changer.

      As the demand for robotic process automation increases market participants include Uipath, Automation Anywhere and Blue prism.

      Here are some of the top globally trusted RPA service providers:

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    • Ruby on Rails' Big Place in Marketplace Development

        How do you know that it’s time for you to launch your online marketplace? When do you see the necessity to provide the ultimate clear and fast “brand – consumer” communications? The point of an online marketplace is to grow customers loyalty by offering them an ever-expanding range of products.


        A brand, whether online or not, must use its reputation and expertise to make its marketplace successful.


        Let's take a look at the benefits of marketplaces and the reliability of Ruby on Rails as the strongest development tool.

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      • Your company life before and after accounting automation

          The manual work of the accountant went into oblivion. Today everyone makes machines, but, of course, controlled by a specialist. Although in our last project we have carefully worked on the topic of artificial intelligence and some accounting operations can already occur with little or no human intervention. Let's look at what systems we attracted, and what exactly was done to save time on daily routine operations.

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        • Custom Shopify Automation for Your Business

            E-commerce today has become so frequent that it has become almost a standard chain of stores, where most people make purchases. But even online stores are trying to save money by becoming drop shippers and more working on additional implementations, rather than on the maintenance and payment of huge storage spaces.

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          • Analysis of commits and pull requests in Travis CI, Buddy and AppVeyor using PVS-Studio

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              Starting from the version 7.04, the PVS-Studio analyzer for C and C++ languages on Linux and macOS provides the test feature of checking the list of specified files. Using the new mode, you can configure the analyzer to check commits and pull requests. This article covers setting up the check of certain modified files from a GitHub project in such popular CI (Continuous Integration) systems, as Travis CI, Buddy and AppVeyor.
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            • CMake: the Case when the Project's Quality is Unforgivable

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                CMake is a cross-platform system for automating project builds. This system is much older than the PVS-Studio static code analyzer, but no one has tried to apply the analyzer on its code and review the errors. As it turned out, there are a lot of them. The CMake audience is huge. New projects start on it and old ones are ported. I shudder to think of how many developers could have had any given error.
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              • AdBlock has stolen the banner, but banners are not teeth — they will be back

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              • Build tools in machine learning projects, an overview

                  I was wondering about machine learning/data science project structure/workflow and was reading different opinions on the subject. And when people start to talk about workflow they want their workflows to be reproducible. There are a lot of posts out there that suggest to use make for keeping workflow reproducible. Although make is very stable and widely-used I personally like cross-platform solutions. It is 2019 after all, not 1977. One can argue that make itself is cross-platform, but in reality you will have troubles and will spend time on fixing your tool rather than on doing the actual work. So I decided to have a look around and to check out what other tools are available. Yes, I decided to spend some time on tools.

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                  This post is more an invitation for a dialogue rather than a tutorial. Perhaps your solution is perfect. If it is then it will be interesting to hear about it.

                  In this post I will use a small Python project and will do the same automation tasks with different systems:


                  There will be a comparison table in the end of the post.
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