Let’s start with some simple real-lie examples that we’re sure you all must have experienced. 

You watch Netflix and it offers you viewing suggestions.

Twitter shows you relevant tweets on your timelines instead of recent ones.

Quora offers specific answers to all types of questions you ask.

If you use Hubspot for sales & marketing, it makes your life easier by telling the customers that are most likely to buy your product.

You ask Siri a question and it gives you an apt reply.

If all of these things intrigue you, tickle your innovative nerve, and make you want to experiment with emerging technology in your business, this write-up is for you. 

Today, most of the applications, software, websites, tools, and devices that we use are loaded with cognitive ability to learn behind the scenes and surprise us with smart, personalized results. This is made possible with Machine Learning. 

You can also leverage Machine Learning to speed-up your development and even launch new ML models to make your software smarter and more intelligent. 

Understanding What Is Machine Learning

While you have heard the term ‘Machine Learning’ quite a number of times, knowing its actual meaning is important before jumping the boats of development with ML tools. 

In simple terms, Machine Learning is the science that teaches computers how to think, act, and learn like humans. In more technical terms, it is the process of getting computers to make precise predictions based on the data they are fed with. 

These predictions could be anything from getting a response from Siri and filtering spam messages from important ones to spotting people in front of a self-drive car and protecting money laundering or fraud detection. You teach the machines and give them the capability to learn from data. 

How Machine Learning Impacts Development

But isn’t Machine Learning about Artificial Intelligence? So, why do I need to know about Machine Learning when I’m not going to use it in my development in the near future? Well, it’s not the case anymore. CTOs are increasingly incorporating Machine Learning technology in their development for many reasons. For those that want to add versatility to their development, develop more futuristic websites, applications, and other web products, it is imperative to gain knowledge of Machine Learning. 

We’re sure this is not enough for you to get on-board with Machine Learning. So, let’s discuss how this emerging technology will impact web development and disrupt it. 

• Improve Customer Experience: Machines are becoming more advanced than humans and they are better at detecting patterns or anomalies for future predictions. Analyzing your customers’ behavior and the unsystematic patterns of their purchase or browsing, Machine Learning responds in a more precise manner to convert. 

• Improving & Speeding-Up Web Development: Machine Learning can be used for analyzing the complaints or the bottlenecks in your user experience to know where the user is facing difficulty. These flaws can be examined by the ML tools and help your development teams correct the issues for speedier development.

• Offer A New Approach To Data Mining: The conventional data mining techniques helped humans detect patterns in their user behavior but also made the decision-makers with the actions taken afterwards. Machine Learning eliminates the need for human intervention and smartly detects the patterns and responds in an automated manner; thus, adding delight to customer experience.

• Eliminate Cybersecurity Threats: The majority of the malware attacks or cybersecurity threats tend to have a pattern, which can be easily detected by Machine Learning bots and tools. They can also detect the phishing attacks on the basis of factors like data encryption, domain, security features, etc., to check security and divert any cybersecurity threat. 

• Personalize Content: Personalization is at the core of conversion right now and Machine Learning can enable you to do that. Like Facebook uses Machine Learning to customize each user’s newsfeed, you can also use this technology to create a personalized experience for your users by delivering content they might like. 

• Discover Products Faster: It can make your customers have a more delightful web experience by helping them reach the relevant product faster. 

Any company’s goal is always to have a software that integrates with its offerings and satisfies its target users’ intent. By using Machine Learning tools, you can infuse your software with cognitive ability and offer smarter predictions. 

Machine Learning Tools You Can Use

When it comes to development, incorporating the latest tech is always an advantageous bet. There is so much you can do by using these emerging technologies and Machine Learning can certainly act as a game-changer. And Machine Learning tools make it even easier. Here is a list of not just the tools that are used to create a state of the art Machine Learning models and applications but are also tools, powered by Machine Learning, which make programming and development easier. Let’s have a look.

Machine Learning Tools For Assisting In Development

• Kite

A great tool that helps all the developers in coding, Kite ensures auto-completion of codes as you write them down. It uses Machine Learning to learn codes from the data that it collects from Python codes. It is rightly called the ‘the AI copilot for Python programmers’. It is not the similar auto-suggest algorithms that you find in IDEs; it goes far beyond this. 

• Integration of Kite is available for almost all the code editors like Atom, Sublime Text, PyCharm/IntelliJ, Microsoft Visual Studio Code, and Vim
• It learns from the Python code publicly available on Github and trains on the abstract syntax tree, giving it a sense of context and intent
• Has the ability to suggest even complete function call with the arguments and respective meanings

• DeepCode

Another tool that enables your developers with languages like Python, Java, and JavaScript, DeepCode ensures quality coding. It analyzes the codes for programmers and helps in improving it by finding and fixing bugs. The best part is that it can easily find even hidden code bugs that might go undetected. 

As per Boris Paskalev, a founding member of DeepCode, “We have a unique platform that understands software code the same way Grammarly understands written language. This unique proposition is positioned to save billions of dollars within the software development community with our first service and then to be on the front end of transforming the industry towards fully autonomous code synthesis.”.

• Codota

Using a cloud-based service, Codota, a Machine Learning model, helps speed-up and ease the development. It offers suggestions for code completions when you type languages like Java and Kotlin. When you are building models using Codota, it not just uses the text of the coding language but also the code’s syntax tree for smarter development. It uses “minimal contextual information from the currently edited file that allows us to make predictions based on the current local scope.”

• The suggestions offered by this tool are a result of the knowledge it gained by going through millions of open-source Java programs. 
• This plugin protects your codes by not sending full code block outside the IDE
• Your code data is not stored on the Codota server
• Error-prevention and faster coding are the benefits you get 
• Codota editor support offered for IntelliJ, Android Studio, and Eclipse but also available for Linux, MacOS, and Windows

Machine Learning Tools For Developing ML Models

• Apache Singa

Initiated by DB System Group in 2014 at the National University of Singapore, Apache Singa is a Machine Learning software that enables natural language processing and image recognition. This software can run on a wide range of hardware and applied for different applications. 

• The memory footprint and training speed of SINGA is optimized by building a computational graph
• With a Python interface and a very easy-to-use software stack, SINGA is known to be improving the usability
• It offers enhanced IO classes with which one can write, read, encode, and decode all types of data and files
• If you need to build a more advanced machine learning model, you can use Tensor abstraction for that

• Google ML Kit

When it comes to developing mobile applications, whether for Android or iOS, there is no better Machine Learning tool than Google ML kit. With this tool kit, mobile app developers can easily build and add personalized features in their applications. Using app-based APIs, you can create a more personalized and optimized app with features like face detection, landmark detection, text recognition, barcode scanning, and many more. This tool can certainly help you build a mobile application that leads you to digital transformation in 2020. 

• Create apps that run on-device as well as on the cloud
• Get an integrated platform of Google’s Firebase mobile app development
• Add video & image analysis for detecting faces, text, barcodes, etc.
• Offer its features to identify and translate up to 58 languages for reply suggestions

• Caffe

When you are planning to add Machine Learning to your Java Development, Caffe is the tool you should use. A deep learning framework, Caffe offers modularity, expression, and speed in your development. It has a single indicator configuration that allows the developers to seamlessly switch from GPU to CPU and back. Written in C+, this framework has a python interface that makes it easier for the developers to use. 

• Expressive architecture that allows you to be innovative and creative
• With an extensible code, Caffe enables the developers an active development environment
• It is one of the quickest frameworks for image processing and can process over 60 million images in a single day, thus learning faster

• TensorFlow

One of the most popular Machine Learning tools among all the CTOs and other companies would certainly be TensorFlow. It was created by the Google team and helps in building excellent Machine Learning models in a quick and efficient manner. You can use the APIs to create the models and even train them as per the data you want to feed. This offers a complete JS library, resources, and tools, for simpler development. 

• You can find it for JavaScript, mobile & IoT, and even a more end-to-end platform for creating ML models for large production environments.  
• Use data flow graphs to do numerical computations
• Deploy and train ML models in the browser, on the cloud, on-device, or on-perm
• Get pre-trained models that have been built by the Google Community 

Wrapping Up

Machine Learning is one of the most powerful emerging technologies that is disrupting everyday lives. CTOs can make use of these Machine Learning tools to speed-up their development teams, fix bugs, and improve the quality of their codes. Apart from that, they can also use some Machine Learning tools to develop new ML models and add state-of-the-art features and functionalities in their software, web apps, mobile applications, websites, etc.