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Want to Outshine Competitors?

Winners and losers are being declared every day in this competitive marketplace. The pressure to innovate business models is mounting. Businesses must be able to find and scale market insights on demand to climb the ladder of success. So, what precisely is preventing companies from delivering measurable business value? 

Winning In Your Business With AI

how machine learning frameworks pays off to businesses

Today companies create a data center to scale everything related to their business and technology. Companies are at a critical juncture and they must find a solution for organizational success. They need a new and modern approach. Scaling everything in your business requires thinking, intelligence, and technology.

But this doesn’t occur very often. However, artificial intelligence (AI) can be the solution to these pressures. AI is all about bringing human features to machines. And, “Enterprise AI” is all about solving small to complex business problems by creating highly dynamic environments. See below the global “Enterprise AI” revenue stats.

Stats for the revenue generated by AI and Machine learning frameworks Of course, the need for AI varies from organization to organization. In some cases, companies jump straight to adopt AI technologies in their business operations. While in many other cases, organizations begin to build their enterprise environment by getting their business analytics and technical advancement in order.

Machine Learning Frameworks

Structure of Machine Learning Frameworks AI and machine learning frameworks helping empower organizations with the most intuitive and sophisticated tools to manage and analyze their mounting data. No enterprise will scale the ladder of success without having powerful AI models. 

So, if you are looking for truly innovative AI and machine learning software solutions to solve your business problems, you should hire machine learning development service that is specialized in a wide array of industry verticals and cater to all kinds of enterprises, startups, and small businesses.

With the best AI and machine learning frameworks in hand, more organizations today are unlocking the true potential of technology. It’s true that AI is becoming a fundamental need as the internet and mobile. Not having AI and ML strategy in 2019 will be like missing out on an important strategy in your business. So to fully leverage AI, you must also understand and know about the best machine learning frameworks and libraries to use in 2019.


We have researched and selected the list of top 10 artificial intelligence and machine learning frameworks that just fit well to business needs.

Moving forward with AI and machine learning programs or initiatives requires a bit of your attention because every application you develop will require a unique set of features, tools, ML frameworks, and algorithms.    

Let’s explore in detail the list of top 10 machine learning frameworks and libraries.

1. TensorFlow 2.0 Beta

Tensorflow 2.0 Beta Machine learning frameworkOf all the excellent options available for machine learning frameworks, Tensorflow machine learning framework is the best and the most mature one. It is also the easiest framework to learn. We are already seeing how the TensorFlow community is growing. TensorFlow 2.0 Beta is a major new release featuring new updates and focused on ease of use and simplification. It has gotten over 128,000 stars and been forked around 75,000 times in the GitHub repository.

So, are you ready to convert your existing code to TensorFlow 2.0 Beta?

Or, in case you’ve not heard about its new version, you’re probably thinking: What’s new? Yet another ML framework update! However, there are major changes available in the TensorFlow 2.0 Beta. For example, Eager Execution is the central feature of 2.0 that make the programming model better with the TensorFlow practice and make Tensorflow easier to apply in coding for AI and ML app development.

Tensorflow 2.0 has got some fairly flexible API choices that let users build AI/ML applications and software quickly and by taking full control over custom models. Many more add-ons and updates are there. 

Tensorflow 2.0 is perfect for someone who is just looking to add an AI/ML application to their business as it is perfectly suited for a vast number of fields such as medical, bioscience, finance, and tech. So, hire machine learning developers to make your first app ready on Tensorflow 2.0 that is one of the best machine learning frameworks to use for AI/ML programming. 

2. Torch

Torch Machine Learning FrameworkFrom healthcare and marketing to security, machine learning frameworks have been changing the way we develop business apps to solve daily challenges and everyday tasks. We have already discussed TensorFlow 2.0 Beta for your ML app projects. But now let’s look at the free and open-source machine learning framework that allows creating AI/ML apps without spending much time and resources.

Torch is often called the best deep learning and machine learning framework for beginners. Despite a less popular framework, it’s used by Facebook, Google, and Twitter for their AI projects. The greatest benefit of Torch is its amazing interface via LuaJIT.

The goal of Torch is to provide maximum flexibility and speed in building your apps based on scientific algorithms. As of now, PyTorch has seen a high level of adoption by the developers’ community. It is basically the port of machine learning framework used for constructing deep neural networks and apps that require high accuracy in terms of computations.

pytorch machine learning framework

As opposed to Torch that uses C/C++ and CUDA libraries Pytorch runs on Python, hence produces simple coding that allows codes to seamlessly integrate with Python functions and other Python packages. Both Torch and PyTorch have been used in many open-source projects related to chatbots, text to speech, machine translation, text search, and image & video classification.

So, what is your idea?

Simply build your new business app on Torch or PyTorch both are highly recommended for the deep learning environment. Hire machine learning developers to make your app ready on Torch the best machine learning framework for Healthcare application development, robotic automation, creating business apps, and web models.

 3. Caffe2

Caffe 2 Machine Learning FrameworkWhile discussing the benefits of Caffe2, let us first declare that the popularity of TensorFlow is huge and Caffe2 is keen to become big soon. Caffe is not so popular yet it’s capable of building AI apps with different neural network architectures more easily.

Caffe’s2 biggest benefit is speed. It allows process over 60 million images on a daily basis on a single GPU. Caffe2 is also the best machine learning framework for creating apps with the features of the deep learning network and visual recognition. However, using Caffe2 help developers to improve the performance, efficiency, and quality of AI and machine learning systems.

Caffe2 not only allows developers to add more features to your app for the general-purpose but allowing developers to create chatbots, perform machine translation, speech recognition, and adding image classification to your business application. 

So, whether you are looking for agile development of a single application or entire suite of apps based on the machine learning frameworks, approach the industry’s best machine learning developers to make your app ready that can bring you exceptional business results.

4. Amazon Machine Learning

Amazon Machine Learning Framework

The beauty of Amazon Machine Learning is that it gives you the ability to develop ready-to-use AI apps and software solutions. Amazon’s AWS is a vastly popular machine learning framework that is also used by thousands of businesses from across the globe. However, there are many more reasons for which developers are switching to AWS.

The first one is Amazon SageMaker that is a fully-managed machine learning framework that you can use to quickly develop, train, and deploy high-performing ML models in your business.

AWS Machine Learning Framework Structure

Accelerate your application development with Amazon’s Rekognition that is an image recognition service based on deep learning. Similarly, Amazon’s Lex is for building voice and chat text chatbots. Amazon Comprehend is for NLP (natural language processing) and Amazon Transcribe gives your app the ability of automatic speech recognition.    

So, which one do you wish to add to your next business application?

The NLP automatic speech recognition or both? The choice is yours! We can only give you the idea of creating your best eCommerce software with the best AI features and ML capabilities. Hire machine learning experts to quickly and easily build, train, and deploy your AI application on AWS.

5. Scikit-learn

Scikit-Learn Machine Learning FrameworkScikit-learn is neither new nor less popular from other machine learning frameworks as per its lifetime. It has been learning from the successes of TensorFlow and AWS and become one of the easiest and cleanest machine learning frameworks to use for application development. Best of all, it is based on Python.

So, if you’re looking to go for deep learning development on Python, then Scikit-learn is the best choice.

The robustness of Scikit-learn makes it perfect to use for machine learning projects. It’s also flexible to use, yet powerful for building applications for the research endeavors. Scikit works on a set of libraries including NumPy, Matplotlib, IPython, SciPY, Pandas, and Sympy.

Why we have mentioned all these libraries? It is because based on all the libraries you can add scientific and technical computing features to your application along with data handling, manipulation, and analysis.     

Beyond all these features, Scikit Learn’s programming and documentation are exquisite! Each of the coding algorithms is explained clearly.

So, if you’re taking your first step toward AI and machine learning frameworks, then it is a no-brainer to opt for Scikit-learn given that comes loaded with the abilities of Python, rapid deployment of new apps, and documentation to guide you.   

6. Keras

Keras machine learning frameworkThe greatest benefit of Keras in the war of machine learning frameworks is its deep learning library in Python. The way we see it, Keras machine learning framework is easy to use, user-friendly, modular, and easily extensible. It can be your best friend if you are looking for AI app development plus deep learning. It is excellent when you have the need for creating massive models of deep learning, as with Keras massive models can be reduced to single-line functions. It is also easy to handle for machine learning developers

Keras is built on neural network APIs that helps you build more exotic applications. It’s an ideal choice for developers that are just starting out. The simple concepts of prototyping help understand the various processes of various models and their implementation. In particular, Keras modularity help combine neural layers, activation functions, and app regularization schemes to create new models.

Keras machine learning framework is seeing widespread and rapid among developers and the business community. In fact, the reason why Keras is popular is because of its lightweight architecture making it easy to use for beginners. 

Keras Github Status

keras machine learning framework stats

These are some fairly good reasons why startups should opt for Keras machine learning framework. Keras can scale up with your app development needs so that you can start working on the updated tools and libraries with best practices.  

7. Apache MXNet

MXNet machine learning frameworkThere are many reasons for which developers are switching to MXNet. The first one is, It is one of the brilliant machine learning frameworks that allow you to prepare profound machine learning/deep learning models without compromising code quality and performance. Instead of being forced you to do everything the MXNet way, it allows you to structure AI/ML application the way you want it to be.

MXNet framework supports all the main development language such as C++, Python, R, Julia, JavaScript, Scala, and Go. It’s only an ml framework that you can use it to bring intelligent features to your iOS application. The framework makes it easy for developers to add the features of machine learning to their app with the Gluon interface that help get started with deep learning on the cloud. 

It is also good for newbies because of fast problem-solving ability. Although, MXNet is not a popular machine learning framework, with its symbolic programming style it is so easy to use for developers and good for the development of your business app. So, if you’re a startup, we recommend that you should hire mobile application developers to create your app on MXNet framework.    

You can also get started with MXNet on AWS (Amazon Web Services) which is a platform to build, train, and deploy applications on machine learning models.

MXNet on AWS machine learning frameworks

What Is It Good For?

The main advantage of Apache MXNet on AWS is you can use it to build artificial intelligence-based applications with greater ease than Keras, TensorFlow or Scikit learn. 

It is an extremely good machine learning framework to handle large development projects because workloads can be distributed across multiple GPU that saves time and increase productivity.

What Next?

If you are looking for something more, in that case, the answer lies in a number of factors such as resource requirement, usage, and your business needs. However, if you’re looking to get started with your next AI and machine learning development, then the machine learning framework from the above list would best suit your business requirements.      

In today’s time, many businesses are opting for machine learning frameworks like Tensorflow, Torch or AWS are ideal. With these frameworks in hand, more businesses are unlocking the value of app development. But to fully leverage AI and machine learning, businesses must also understand how to adopt and implement the technology. 

We hope you learned something new and useful about the list of top machine learning frameworks. If you did, feel free to comment on the below-given section.

At ValueCoders, we extensively work on machine learning frameworks such as TensorFlow and Scikit-learn for our client’s requirements. Being the leader in the machine learning development niche, we always try to excel in the market by offering truly innovative AI and ML-based software solutions that can solve your business issues by simplifying various tasks and save a lot of your time spent on business operations.

ValueCoders is a known name in the Indian software development industry focused on delivering offshore software development services. With 14+ years of experience, 2,500 satisfied customers, 97% of client retention rate, and 4,200+ projects, we have worked with many startups, software product development companies, digital agencies, and enterprises and helped them by simplifying their software and application development needs in less cost and time. 

ValueCoders means peace of mind that gives you full control over your projects and you also have the flexibility like your in-house machine learning development team.

Winning With AI and Machine Learning Frameworks

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