How Enterprise Software Is Getting Intelligent Through Machine Learning

Developments in computing have caused Machine Learning(ML) to come closer to what writers and inventors foresaw a long ago. AI, ML and Neural Networks have become the focal points of innovations and developments in computing. They have moved closer in order to achieve their full potential and are currently finding applications in almost every field of human activity from transportation to agriculture. Furthermore, these are the evolving forms and they have a notable effect in the enterprise and computing field too.

 

Among them, ML has found widespread acceptance among many enterprises and organizations. By integrating ML into enterprise software, many companies have discovered a much more predictable and data-driven model, that allows them to automate their business processes. Machine learning’s core dynamic of utilizing data sets has helped generate insights, that promote innovation, efficiency and have made enterprise software much more intelligent.

 

With ML, computers can mine out and process small to huge quantities of data in order to come up with predictive models and insights. A typical example that you can find of ML at work is in e-commerce where online retailers like Amazon are using this technology for offering better services to its customers.

Read also: How to make Chatbot using ML

Each time you make a purchase or browse through the products list, you allow to  leave a trail of data used by its ML systems in order to give you personalized product recommendations. Besides, the use of ML is giving e-commerce platforms a clear edge when it comes to customer service, pricing, as well as delivery.

 

Why Machine Learning will impact Enterprise Software

Every day, the organization or enterprise is riddled with challenges from management to operations. Enterprise software up to now has played a major role in tackling many of these vital processes. But the rising demands in business have meant a new approach is needed to deal with the diverse fields. With the introduction of ML into the enterprise, a window of possibilities was left open.

ML is providing the right framework that organizations can rely upon. The algorithms and applications used in a ML embedded computing system can easily bring out predictive insights by harnessing the huge data sets owned by these enterprises. These data include both structured as well as unstructured forms such as databases and the Internet.

According to a study by MIT Technology Review as well as Google, 60 percent of companies have already adopted an ML strategy into their computing infrastructure. 18 percent plan to implement ML within the next year.

 

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The switch to an ML software model has clearly benefited large corporations by giving improved and faster data analysis and insights. Besides, the complexity of ML is preventing companies from switching to such a model. Being currently in development a complete shift to an ML model can prove to be unwise. ML has several restrictions and isolations as its algorithms frequently encounter errors and resolving them is an ardent task due to its complexity.

Besides, it can take time for an ML embedded software to familiarise with the data for better predictions. Meanwhile, these predictions may not always be accurate in all scenarios where historical data is not unavailable. The need for human intervention is still relevant to extract the desired output from ML enterprise software.

ML is identified as a major trend among enterprise software in the current year. By making enterprise software intelligent, ML can take over the crucial process of assessment by creating valuable insights. An enterprise or company can use this to take the necessary action to their productivity is increased.

Incorporating an ML framework into an enterprise software ensures all the data stacks are churned out for improving overall productivity and efficiency. This actually enhances the capabilities of software used in the enterprise thus making it much more intelligent as well as self-sufficient.

For example, in the area of sales, a company can gain a lot by using software having an ML model. ML can disrupt crucial processes and create more understanding of forecasting, customer behavior, and its assessment to make the sales process more intelligent.

Here are some ways by which ML will transform enterprise software are:

 

  • Generating better analytics and Insights

 

Data usage and data generation have increased considerably in organizations in recent years. Processing these large amounts of data is a huge challenge, which is where ML fits in. Having enterprise software with ML helps derive a competitive advantage from these large sets of unstructured data.

By supplying the data gathered, the algorithms in an ML embedded software can easily process these data sets and discover various insights as well as patterns. An organization can benefit greatly from these results by helping them in order to meet their objectives and drive the growth forward.

Recommended reading: NLP for chatbot

The multinational retail corporation Walmart is a fine example of successfully implementing ML software. They utilize ML heavily to discover customer behavior and usage in areas such as product recommendation.

As a result. Walmart has dramatically improved its retail operations and customer experience. Besides, ML helped them create a bridge between their shopping experiences offered at their stores and online platform

 

  • Delocalization of Data

 

Data is now largely scattered due to the use of various technologies like mobile and social media. Typically, enterprise software used specific kinds of database models in order to store as well as organize data. Structured and unstructured data can now exist together in external sources, which requires deeper processing for enabling the systems to generate useful end results.

Technology giants like Google have already shifted entirely to external storage modules in the form of data centers for handling its massive troves of these unstructured and structured data. These data centers are located in different places around the world and require constant monitoring as well as maintenance to make them running all throughout without any disruptions.

Google has already utilized ML software to aid in this process, which has resulted in a significant decrease in energy usage amounting to a 50 percent reduction in 2014 alone. The tech giant was able to optimize and save much more energy in its data centers by using an ML model.

 

  • Facilitate Data-driven Decisions

 

The use of data in an effective manner can prove to be of immense advantage to a company in formulating crucial business decisions. Enterprise software systems that have been using ML can aid in this process greatly by helping companies and enterprises to make decisions solely and impartially based on the insights gathered from this pool of data. This will be of particular help to areas like talent hiring, customer management, R&D and so on..

For example, the major use of TensorFlow technology, Google’s open-source library for ML applications, has already enabled researchers departments in companies for getting yield in the potential of ML for delivering better insights to enable the development of products and services. The use of DMAIC (Define, Measure, Analyze, Improve and Control) is common when ML is applied in R&D to make data-driven decisions. This has led to a considerable improvement in the quality of products and services.

 

  • Empowers Employee Intelligence

 

 

ML takes enterprise software a step forward by automatically assessing the searches that employees in an organization make. This has proven to actually empower employee intelligence by facilitating decision making in areas related to work by monitoring all the crucial areas that they engage with.

In this manner, ML can assist in enhancing productivity, save time as well as give timely responses. For example, IBM has set a model on how cognitive technologies like ML can empower the employees by creating better engagement and personalized dissemination of information for purposes like coaching.

 

 

  • Fraud Detection

 

 

The continued presence of fraudulent practices contribute to losses to an organization every year. Enterprise software with an ML framework have helped bring down the fraud by detecting it early. The pattern recognition methods used by the ML algorithms can identify any irregularities in operations like transactions.

 

An example of how ML would help in fraud detection is in the financial section of an enterprise. By including ML in enterprise software, it can evaluate the transaction data and external sources to detect any kind of fraudulent activities as well as anomalies in the transactions involving a network of individuals.

 

SAS is an early adopter of ML in areas like fraud detection. In the finance sector, SAS has ventured into fraud detection and the use of  their software by several credit institutions to eliminate fraudulent practices in transactions.

 

Conclusion:

There is no doubt Machine Learning is taking over the world and helping organizations and corporates get the best out of their businesses. In the coming years, we will see ML be booming in every domain and sector possible, and helping our lives to be augmented and amplified further to improve the quality of life that we live.

If you are thinking to this all this as in real for your development then Hire Python Developers from Nimap Infotceh directly contact and get quote

 

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nimap October 23, 2019 0 Comments

PHP vs Python

 

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Before we begin about which language is better for web development. Let us start with the basics of each language. The language used for web development in python and PHP. We are also going to find out how python is better than PHP when it comes to web development

 

PHP (Hypertext Pre-processor)

PHP is a scripting language that is designed for web development that is used to create dynamic as well as interactive HTML web pages. This is also a general-purpose language that is used for programming. This language was introduced by Rasmus Lerdorf in the year 1994. The full form is known as Personal Home Pages but it is now known as Hypertext Preprocessor.

 

PHP is nothing but an HTML based web Scripting language. This means that PHP code can be inserted in between HTML snippets of a web page. It is the fastest way to create database-enabled as well as dynamic websites. PHP Supports enterprise as well as main web servers such as Apache, Microsoft IIS, Netscape, etc and it also works on operating systems such as Linux, Unix, Windows, and Mac OS.

 

We have listed down some key features as below:

PROS/ Advantages

 

Open Source:

It is based on the Open source programming concept and this means that you do not have to pay in order to use it. You can download for free and use the PHP language.

 

 

 Know more: Basic difference in Web Development with Core PHP and PHP Frameworks

 

 

Simple and easy to use:

When you compare with other scripting languages, it is very simple, easy to use and understand and is used widely all over the world. Its robust and secure features provide it an advantage to use and implement in web servers for serving PHP and HTML web pages

 

Interpreted language:

PHP is an interpreted language, and this means that a compiler is not necessarily eliminating the need for any compilation process.

 

Platform Independent:

PHP code is platform-independent which means that it can run independently of any platform such as Windows, Unix, Linux, and Mac OS, etc

 

Speed:

PHP is comparatively faster than any other scripting language since it uses much system resources.

 

Error Reporting:

PHP has a function for error reporting in order to generate the warning so that one can easily find the errors as well as fix them on time.

 

 

Disadvantages:

 

9Security:

As we already discussed that the language is based on the Open Source License. All the people that are using it can access the source code and if there exist any bugs in the source code it can be used to explore the weakness of PHP.

 

Unfit for modular applications:

It is hard to maintain the applications as the language itself is not very modular.

 

 

Let’s discuss some basics of python.

 

What is python?

Python is a high-level programming language that is used for general-purpose programming. The language was designed and developed by Guido van Rossum. It was released in 1991 and has kept a design philosophy that puts emphasis on code readability notably using remarkable white space.

It is used for the following, Software development, web development, mathematics as well as system scripting. There are 2 major versions of the python language, Python 2 and Python 3.

Python has a natural style of writing which makes it easy to read and understand as well as it is equally fun to use as well. Some reasons’ python as gained popularity because:

 

 

Beginner Friendly Language:

It is a very user-friendly language that is beginner-friendly because it is easy to understand as well as fun to use. That is one of the reasons that python has gained huge popularity to be a beginner-friendly language.

 

High-Level Language:

As it is a high-level programming language so while you are using the python language the user need not worry about low-level details such as memory management.

 

Shorter code:

Another advantage of using python language is that it is well known for producing shorter codes when compared to other programming languages to achieve the same task

 

Clear Syntax:

Python does not use curly braces, it has a very clear, orthogonal and brief syntax.

 

 

Read More: 5 Reasons Why You Should Choose Python for Big Data

 

 

 

Object-oriented:

Python supports both procedure-oriented programming and object-oriented programming. It has a very powerful but easy way of performing object-oriented programming compared to other programming languages

Now, let’s look at some of the main reasons for why to prefer using python over the PHP language
If we talk about the readability as well as maintainability of both the languages PHP is not much maintainable as compared to Python.

Python consists of more security features when you compare it with PHP.

In PHP, the functional programming is not provided but in python, functional programming and techniques are possible.

PHP was designed with the sole purpose of being as a ‘web development’ language while Python was designed with the sole purpose of being a ‘general purpose’ language.

Python has a more united and commanding standard library, and the library is much better than that of PHP
Python has a more dense and clean syntax that is easily readable that helps developers in web development.

 

Conclusion:

So now we know which language is best suited for which environments and how we can use the language with the best of the features to satisfy and complete a programming task. We hope that you have found this blog helpful. If you are looking to hire PHP developers for any web designing project then do give us a call to discuss your requirements. If you are looking to hire python developers then also provide us with your requirements. We are there to help you design that masterpiece website.

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nimap September 4, 2019 0 Comments
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