Do you know what role Machine Learning plays in different industries?

Behind the flourishing success of industries, Machine Learning has given an indelible contribution.

Read more info at - https://www.oodlestechnologies.com/blogs/An-Industrial-Revolution-Sparked-By-Machine-Learning

Do you know what role Machine Learning plays in different industries?

The idea of Machines learning from the past experiences has always intrigued the world and many tech enthusiasts have given up their efforts to make it happen. Machine Learning has been a crucial part of Artificial Intelligence and it deals with subjects like pattern recognition and computational learning. To say the least, Machine Learning is all about designing algorithms that enable the computer to learn from data. This further helps in making smart decisions and important predictions for the future events.

Behind the flourishing success of industries, Machine Learning has given an indelible contribution. Leading Tech giants like Microsoft, Google and Amazon are now providing cloud and web services for Machine Learning. Microsoft’s Azure platform is among the first few to offer inclusive cloud services for the Machine Learning and Predictive Analytics solutions. The Azure Machine Learning Studio is a part of Cortana Intelligence Suite, lets you publish different models as web services. It also lets you build and deploy Predictive Analytics solutions based on the data you have. The main purpose of creating the Cortana Intelligence Suite was to help customers gain easy access to Big Data Analytics and Machine Learning technologies by virtue of Cloud. Since the day of its inception, Cortana Intelligence Suit has been helping different industries in uplifting their strategies. Let’s see what role Machine Learning has to play in different industries.

Also read How Machine Learning Will Aid Neural Science.

Manufacturing

Machine Learning has greatly revamped the manufacturing process and a large number of companies are now using Machine Learning algorithms to alleviate the manufacturing process. Over the last few years, there has been a radical incline in the amounts of data being produced by the manufacturing industries on daily basis. It has further aggravated the deployment of a system that helps in processing and analyzing such massive amounts of data while learning and preparing itself for the future events all at once. That’s when Big Data and Machine Learning come to the rescue. While Big Data Analytics help in processing and analyzing massive amounts of data, Machine Learning plays a significant role in making smart decisions. Advanced techniques like Smart Manufacturing, Smart Factories and Industrie 4.0 are a few examples of Machine Learning applications.

Medical And Healthcare

Machine Learning is widely being used in healthcare and medical sciences. Some of the applications include Remote Patient Monitoring and brain signal decoding. Microsoft’s Cortana Intelligence Suite is readily being used for remote patient monitoring where the health conditions of a patient are monitored. Scientists are also finding ways to decode the electrical sensations produced in human brain to read the thoughts of human mind by means of Machine Learning. A lot of research is going on in this field and numerous experiments are being conducted.

Finance Management

A large number of financial companies and FinTech giants are also using Machine Learning techniques to boost their business and improve their user relations. In finance, Machine Learning is mainly used for the classification of financial documents, underwriting of loans, portfolio optimization and much more. Machine Learning serves as one of the key tools for financial planning and is used for making important financial decisions based on the current data. Apart from that, it is also used for determining the credit rating of a borrower by monitoring his/her transaction history. In short, Machine Learning is a crucial financial asset which greatly extenuates and streamlines the financial processes of an organization.

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Why Machine Learning Seems To Be All Around These Days

In our day to day life, we all must have experienced machine learning. When you type something and spell a word incorrectly on google search it still shows you the right results that’s where a machine learning application is implemented. Social media services like “people you may know”, “face recognition” are also a part of machine learning applications. We can say that it is everywhere nowadays. Probability is that you are using it in different ways without even releasing. Machine Learning is a buzzword in the world of technology and even if you are not familiar with all the technologies you would have heard about this term.

Let’s try to understand what is Machine Learning precisely ? And how it is revolutionizing the field of artificial intelligence.

Machine learning is a section of Artificial intelligence which is targeted on the concept where machines can learn and make decisions for themselves. Learning is an important part of human life. To implement AI into the systems that can show intelligence like humans then machines should be able to learn and adjust based on their last experience. It basically explains what machines can do with learning.

We come across many applications that are driven by Machine Learning in our daily life. Some examples of these are Facial, voice or object recognition, Anti-spam mails, forecasts of weather, predictions about traffic and search engine for improving search suggestions. We all know about spam mails. The email system uses spam filter to differentiate between emails that are worthwhile from spam emails of no use.

You may also like: An Online Platform For Deploying Machine Learning Models

Machine Learning has become a significant part of artificial intelligence. Machine Learning and artificial intelligence are not same yet they are often combined into one term. ML is a branch of AI and without machine learning, artificial intelligence signifies nothing. AI is the term that enables machine to do task intelligently. On the other side, Machine learning indicates the automated process that machines make use of to recognize meaningful patterns in data.ML is the most dominant thing in today’s world and also in the future of technologies. The progress in ML is reshaping the future of industries. It is powering the AI development currently. Self driving cars are the most talked about topic today.

By machine learning, technologists have copied the way human mind works. It has developed complex frameworks called neural systems. Thus, neural systems make deep learning possible, a result that has delivered computers superseding human intelligence.Machine Learning algorithms deal with a large amount of information that give way to technology to make predictions. The best example of this is Amazon’s suggested product feature. It looks through your preferences and the buying habits of other people, and then suggests you other products that might appeal to you.

By using ML algorithms, we can build models that discover connections. Also, organizations can make better decisions deprived of human intervention. There are lots of ways where ML has been rendering its potential for empowering Artificial intelligence.

Read more info at — https://www.oodlestechnologies.com/blogs

Reshape Your Services With The Cloud Development Technology

Reshape Your Services With The Cloud Development Technology

The lightning transformation of cloud development technology is allowing enterprises to deploy services more securely, measure them, and modify them at a much faster pace. At the same time, if they are not coping up with the evolution, it can swiftly lead to ending up with a dainty cloud implementation. However, as the stupendous advantages of cloud development become highly recognized, most organizations are striving to amalgamate the way they develop for the enterprise and the cloud. Also, it is a little difficult for the organizations to keep up with the development of new cloud development tools and practices as they are being updated almost as frequently as possible. Keeping that in mind, here are few revolutionizing cloud technology developments that the enterprises can never afford to ignore.

Microservices’ Shift To Kubernetes

Microservices are becoming highly penetrative; everyone is either deploying in accordance with that or at least planning to do so. And as more organizations undertake a pure Kubernetes approach to cloud development, it is, therefore, becoming the major “Operating System” of the microservices.

Also Read How To Improve Cloud ERP With AI And Machine Learning

Kubernetes not only initiates containers and allows basic connectivity for enabling microservices to function together but it is also non-opinionated and permits businesses to implement various tools to sweep out the chunks that it can’t really handle. In addition to this, local storage is not the desirable stateful storage area, instead, enterprises should include microservices with high-performance network storage.

Service Mesh Integration

The most recent advancement of cloud microservices development inculcates using tools known as service meshes. Basically, it is a proxy between microservices that assists with networking glitches and also provides several features that reflect what is happening in your services or applications.

Putting service mesh to use is almost similar to messaging platform integration tool since some features could permit simple integration of latest service-level enhancements, thereby, stimulating organizations to build services that are more reliable and fault tolerant.

DevOps’ evolution to GitOps

Since microservices are capable of populating tens of hundreds of containers, “infrastructure as code” or “GitOps” is becoming more visible as the standard approach for recognizing where microservices are standing, hence, ensuring enterprises to function more effectively.

Although GitOps is an evolution of DevOps, however, it does not really change the basic idea of DevOps and thus can be continued to be used as earlier.

Serverless Functions

The most essential aspect of serverless computing is that organizations are only invoiced for the duration these functions are executed and since there is no such vital requirement of particular nodes, serverless computing can lead to cutting down the cost by over 95%.

Also, the serverless functions including the targeted applications can give out marvelous results and should never be overlooked when you are trying to save some costs.

Also Read Cloud Computing And Its Testing Tools

Therefore, I believe that imposing greater automated testing is essential to ensure that the organizations will be well positioned in the near future with the help of cloud development technologies. What are your thoughts about it? Let us know by writing down in the comment section below or contact us if you are looking for leveraging cloud technology development in your business.

Read more info at — https://www.oodlestechnologies.com/blogs/Reshape-Your-Services-With-The-Cloud-Development-Technology

AWS Advances IoT With New Services

IoT will be the next big thing in the world of technology. There is so much buzz around this technology. According to the statistics, Internet of Things is going to be the largest device market in the world. Owing to its popularity and importance, various sectors of business are recognizing its power and benefits arising from it.

Expectedly, IoT innovations will be embraced by the diverse sections of economy. In this direction, Amazon web services have announced a new OS and IoT services to foster the deployment of Internet of Things. These services will ease down the work process involved in the configuration of IoT devices. What is really important in IoT technology is its data analysis gathered by IoT devices in real time. All of these services by AWS do analysis on the data collected by IoT devices

AWS’s new operating system named Amazon FreeRTOS is designed for low power devices such as smoke detectors, light bulbs. The problem with these devices is that they cannot have their own CPUs due to their small size. It will simplify to build in cloud support for manufactures.

As stated by Tara Walker, technical evangelist for AWS, Amazon FreeRTOS holds a purpose of removing complexities in the development process and deployment process. Also, it extends its support for the maintenance of microcontroller-based edge devices and their security.

AWS has introduced services such as 1-Click, Device Management, Device Defender, Analytics, and Greengrass ML Inference under AWS IoT platform. These services provide help in deploying IoT devices speedily with safe environment. The role AWS IoT platform is to gather data and analyze from IoT devices, sensors. Further, it connects that data to AWS cloud applications. The analytics capabilities of these new services will make use of the massive amount of data collected by internet-connected devices.

AWS IOT 1-click is a service that allows customers to enable device with the AWS Lambda function. For it, they have to download a mobile app and do registration. And then they need to choose an AWS IoT 1-Click enabled device and associate this device with an AWS Lambda function. For instance, sending SMS or email messages. This is intended to do normal tasks like reordering goods, tracking goods location etc.

Another service which AWS Greengrass offers a new feature of AWS Greengrass ML Inference. AWS greengrass service has machine learning abilities which provide tools to IoT devices to make use of machine learning models that let these devices to make decisions more quickly. Still without access to the cloud, it offers these features. Customers can deploy machine learning models on IoT devices easily.

You may also read: Apple Partners With GE On Industrial IoT Applications

Analytics from AWS IoT, is a preview version that cleans, processes, store and analyzes data from IoT devices at scale. AWS IoT Device Management is developed for helping firms to make onboarding easier, remotely manage, monitor devices, update software, troubleshooting and patching defective systems.

According to Amazon web services, Device Defender is expected to be available in 2018. It provides steady examination and monitors for security issues, such as traffic making way to an unknown IP address.

AWS Company has took this step for making IoT deployment process simpler. As estimated ,IoT market will grow upto $457B by 2020 globally. The Internet of Things is reshaping almost every corner of life including homes, offices, city streets, businesses, economy and beyond.

Read more info at — https://www.oodlestechnologies.com/blogs/AWS-Advances-IoT-With-New-Services

NodeJS Web Development Company

Looking for a holistic web development solution? Welcome to Oodles, the best offshore Node.JS Web Development Company.

Node.JS is an open-source framework based on JavaScript runtime environment to execute JavaScript code server-side. It is used for building websites and web applications. This free framework can run on several platforms like Linux, Unix, Windows, Mac OS X and a few others. With the use of JavaScript, Node.js lets developers run scripts to make dynamic page content. Node.js is one of the fundamental elements of the “JavaScript everywhere” paradigm. This allows web application development to unify around a single programming language, rather than rely on a different language for writing server-side scripts. It is also a part of MEAN Stack. Its major use is in backend operations and by the Full Stack Developers.

NodeJS offers cross-platform web app development. It is capable of creating, writing, deleting, closing, reading and opening files present on the server side. Its event-driven architecture is capable of asynchronous I/O and it helps to optimize throughput and scalability in Web applications with many input/output operations, as well as for real-time Web applications. The framework supports all the prime computer platforms like Windows, Linux, and Mac OS X.

Major Advantages of using Node.JS

• Faster code execution
• Has an asynchronous and event-driven architecture
• The single threaded model however highly scalable
• No data buffer
• The issued license under MIT license

Why Choose Oodles Technologies?

We, at Oodles Technologies, provides top-notch NodeJS Development Services. We are a leading Node.JS Web Development Company with more than 7 years of experience and we understand that every client is different with different needs. Our team of web app developers is proficient in using JavaScript as well as capable of building avant-garde websites and web applications. Our only motto is to deliver the best results to the clients and fulfill all their requirements at an affordable price. We have an extensive portfolio of clients from all over the world and excel in providing world-class web development services to our clients.

Read more info at — https://www.oodlestechnologies.com/nodejs-web-development-company

4 Programming Languages You Can Use to Create Chatbots

Choosing an AI (artificial intelligence) programming language is not difficult but it can be confusing. So, if you are one of then who gets confused to pick the right programming language for Artificial Intelligence then you have come to a right place.

1. PYTHON:

Python is one of best programming language for creating Artificial Intelligence chatbots. This object-oriented language is widely used by the developers all around the world. There is the number of reasons behind the popularity of python, such as:

• The large variety of libraries which can be used in AI.
• Less development time
• Easy to write chatbots in Python
• High-level syntax

2. JAVA:

Java is also a great choice. It is an object-oriented programming language that focuses on providing all the high-level features needed to work on AI projects, it’s portable, and it offers in-built garbage collection. Java has some amazing features for the development of AI programming chatbots. They have their own community which is there for you whenever you are stuck somewhere. It provides various features:

• Scalability
• Garbage collection
• Portable
• Easy to code algorithms

Also Read: 4 Programming Languages You Can Use to Create Chatbots

3. C++

There is no other language which can compete C++ in terms of the speed. This has become the advantage for AI. C++ is the best choice when comes to machine learning and neural network building as it is the fastest programing language in the world. Various other features of C++ over other languages are:

• When you need high performance it is a great choice
• Algorithms can be written extensively in C++
• Provides high-level abstraction
• Useful for time-sensitive projects

4. Lisp

Lisp is quite an old programming language but it is still used in developing artificial intelligence chatbots. It is used in many AI projects such as projects, such as Macsyma, DART, and CYC. various features of Lisp are:

• Symbolic structure of Lisp makes it highly used language for creating AI chatbots.
• automatic garbage collection
• Easy coding

Read more info at — https://www.oodlestechnologies.com/blogs/4-Programming-Languages-You-Can-Use-to-Create-Chatbots

How Machine Learning Can Help Fix Your Low Quality GIFs

Creating and sharing GIFs is fun. Apart from emojis, GIFs are becoming the favorite choice for the people to express their feelings while in middle of a conversation on a plethora of chatting apps like Messenger, Telegram, Whatsapp and Snapchat. Some popular apps like Whatsapp provide an easy access to thousands of GIFs owing to the GIPHY integration within the app.

Other than that, there are a number of websites including mr-gif, Gifbin and Littleplasticthings. But the thing is, you’ll find most of the GIFs in pretty bad shape. This is primarily because of the availability of enormous content over the web. Since most of the GIFs are created from the video clips, they are compressed to extremely small size which in turn affects their quality. I know what you might be thinking by the time. It’s easy to degrade the quality of an image or video but it seems like an impossible task do the opposite. Well, with Machine Learning, everything’s possible.

How ML Will Fix This?

There’s a similar website called Gfycat that’s working on solving this issue by leveraging the innovative aspects of Machine Learning. The company is working on the technical front to make the low-fidelity GIFs look good by improving their quality. The website features a wide variety of GIFs in every category including sports, entertainment, fun, dance, celebrities, mood, expressions and emotions.

As per the company CEO, Richard Rabbat, the problem with these GIFs is mainly related to the video and is less to do with the image recognition. For this reason, Gfycat will look for the original video source across the web from which the GIF has been created. Most of these videos can be found on YouTube.

The app will analyse the entire video to find out what part of the video has been turned into that particular GIF. Gfycat will then produce a high-quality GIF from that video and will swap it with the low-quality GIF to make it look better.

The Challenges

The biggest challenge is perhaps creating a high-quality GIF out of a shoddy GIF featuring a celebrity or a public figure. As a GIF is uploaded by a person, he seldom tags that celebrity while making the upload. So it gets difficult to identify which celebrity is featured in the GIF. For this, the company has build up a vast library of popular celebrities. With this, the company hopes to identify the celebrity GIFs coming in and tag them automatically.

Another big challenge would be to identify and replace the text shown in the GIFs as caption. In most of the GIFs that we see, the text is grainy and not easy to read. For this, Gfycat aims on building some internal tools that would help identify what the captions say and then machine learning will rewrite the caption with clear text.

Clearly, Gfycat is not the only one with such an endeavor. Several other similar companies like Tenor and Giphy are also working in this direction. Let’s hope things work out well for these companies and we get a robust platform for refurbishing the low-fidelity GIF images.

Read more info at — https://www.oodlestechnologies.com

Top 5 Mistakes To Be Avoided During ERP Selection

We live in the time of automation. Do you wish to build your own test automation framework? There are plenty of open source options available. There are a lot of tools which makes regression testing very easy. These are some of the best DevOps testing tools.

1. Appium
   Appium is an open source tool mobile application on platforms like iOS mobile, Android mobile, and Windows desktop platforms. It is a great UI testing framework which allows hybrid, native and mobile app testing. The one thing that makes Appium extremely popular is, the cross-platform app testing that allows you to write the test against Android, ios and Windows using the single API. Appium is easy to set up and is made of simple applications. These factors make the Appium so popular.

2. Cucumber
Cucumber is yet another amazing tool based on Behaviour-driven Development (BDD) framework. Cucumber is a DevOps Software tool which is written in Ruby programming language and then extended to Java framework. The Cucumber lets the functional validation in a lot easier and reliable format. The cucumber can be used along with the Selenium and capybara etc. It supports many other languages such as Python, PHP, perl, and Net.

Also Read: 5 Mobile Testing Tools In 2017 For Android And iOS

3. Jenkins
Jenkins is open source Continuous Integration tool which is written in Java. It automates server tasks which include, testing, building, and even software deploying. The installation of the Jenkin tool is a lot easier than others as it comes with the WAR file. Since the Jenkins is written in Java, therefore it is easy portable to other platforms. It is compatible with all of the operating system.

4. Maven
Maven is a build automation tool which is used for Java, Ruby, Scala, C++ projects. Maven lets the project t be created using the project object model (POM). It uses the file in XML to explain the project. It is hosted by the Apache Software Foundation. Maven makes the java programming very easy.

Read more info at — https://www.oodlestechnologies.com/blogs/Top-5-Mistakes-To-Be-Avoided-During-ERP-Selection