Top 10 Machine Learning Frameworks for 2019

Machine Learning

Machine Learning (ML) is one of the fastest emerging technologies today. ML developers are looking for the proper framework for their various kinds of projects for ML application development. Top 10 machine learning frameworks listed here are meeting the contemporary needs of developers in cost-effective ways. Let’s learn about it.


According to Wikipedia, machine learning (ML) is a scientific study of statistical models and algorithms to help a computing system accomplish designated tasks efficiently and independently by relying solely on inferences and patterns extracted from the training or acquired data. It is a sub-set of Artificial Intelligence technology. Thereby, it automatically leans and improves the performance with the pace of time, interactions, experiences, and most importantly, valuable data about the tasks allocated.

Why Machine Learning Framework?

We know in the software industry that without an interface, libraries, and organized tools, software development proves a nightmare. When we combine these essentialities, it becomes a framework or platform for easy, quick, and meaningful software development.

ML frameworks help ML developers to define ML models in precise, transparent, and concise ways. ML frameworks are used to provide pre-built and optimize components to assist in model building and other tasks.

When we choose the proper ML framework for our next project, we must keep the following things in mind.

  • The framework should performance-oriented.
  • It must always provide tradition to create models and provide a developer-friendly tool.
  • It should quick to grasp and code.
  • It ought to out-of-box, not a black-box of a pilot’s cockpit!
  • It has to offer parallelization to distribute the computational process.
  • It must have a significant user-base and supporting the community to help.

Let’s learn about the top ten machine learning frameworks to make the right choice for your next ML application development project. Before dive deeper, I would like to acknowledge you that

  • Some ML frameworks are more mathematically oriented.
  • Some focus on deep learning while some neural networks.
  • Some are rich in linear algebra tools, while some statistical tools.
  • I hope these insights help you ahead in the selection.

1. TensorFlow

TensorFlow has complicated functions and paradigms, so it demands additional efforts while learning it. If you have grasped Numpy arrays of Python framework and its libraries, your job becomes easy while working with an n-dimensional array in TensorFlow framework.
It is a flexible framework and offers multiple models as well as versions, which are serving simultaneously. So, it helps in non-automatic migration to newer versions. The framework can run on the GPU, CPU,servers, desktops, and mobile devices. Therefore, it is portable. It is useful in research and development purposes. It provides auto differentiation and performance.

2. CAFFE (Convolutional Architecture for Fast Feature Embedding)

Caffe 2 is a deep learning tool and highly effective ML framework written in CPP. It supports the central schemes and is connected with neural network design. It provides GPU & CPU-based acceleration. Therefore, Caffe 2 is popular in academic research projects and startup prototypes.

It is the fastest framework for deep neural networks and supports GPU training for sample data. It has a well-structured Mat Lab and Python interface to work. It allows switching between CPU & GPU by setting a single flag to train and deploy to commodity clusters.

CAFFE offers excellent performance and renders it perfect for industrial deployment as well as research experiments. It can process 60M images per day with a single GPU and provides the fastest convent implementation.

3. PyTorch or TORCH

The Torch is an ML library and scientific computing framework. It comes with a scripting language interface from Lua programming UI. The torch was used by Facebook, IBM, Yandex, and Idiap Research Institute.
Torch is flexible and offers high-end efficiencies and speed. It provides a lot of pre-trained modules. The most remarkable thing in Torch is numbers, not an integer, short or double. Thus, it eases various operations & functions.

4. Apache Spark

Spark is a cluster-computing framework mainly written in Java, Scala, R, and Python. It has Spark RDD data structures, so you need not worry about a Numpy array. It also lets you work with Spark SQL frames.

It offers simple APIs for data scientists familiar with R & Python. It is scalable and capable of running machine learning code on small to large machines. It is streamlined end-to-end and compatible.

5. Scikit-Learn

Scikit-Learn is a powerful Python library for machine learning coding and is explicitly used to create models. It has built on other libraries such as SciPy, Numpy, and matplotlib. It is a highly efficient tool for classification, regression, and clustering like statistical modeling.

It comes with supervised & unsupervised machine learning algorithms as well as cross-validation. It is written in Python, but it scores is in Cython for the sake of performance. Cython wrapper around LIBSVM helps in the implementation of support vector machines.

It is highly efficient for data mining and supports various practical tasks. It is capable of dealing with complex tasks with ease and speed.

6. Microsoft CNTK (Cognitive Toolkit)

Microsoft has created it intending to provide a toolkit for deep learning and use it for a training algorithm to learn like the biological brains of humans. However, we can use CNTK to create various machine learning models. For instance, feed-forward DNNs, recurrent neural networks, and convolution neural networks.
It facilitates neural networks to go through vast and unstructured data sets. It offers rapid training time and intuitive architecture. You can customize it as per your requirements because it lets you select your metrics, networks, and algorithms. It supports multi-machine-multi-GPU-back-ends.

7. Apache Mahout

It is an exclusive tool for mathematicians, data scientists, and statisticians. It lets its user execute their algorithms quickly. It is an excellent choice for developing machine learning applications with scalable performance, as it also acts as a distributed linear algebra framework.

It is chiefly focusing on filtering, collaborative grouping, and classification. It lets developers create their mathematical calculations in an interactive environment. The analysis can run on a Big Data platform. Moreover, you can shift the same code in your applications as well as implementations.
It provides a distributed linear algebra and statistic engine, which works and distributes together with an interactive shell and a library to link the application. It can climb onto Apache Hadoop with the help of a map or reduce paradigm, but with enough freedom.

8. Apache Singa

It is the best fit for Big Data analytics. It is a flexible and scalable framework for ML because it provides a flexible architecture for scalable distributed training for Big Data. It runs on a wide range of hardware, so it is extensible. NLP and image recognition as the main application areas for the framework. It supports traditional ML models, including regression.

9. Amazon Machine Learning

Amazon also has wetted its legs in the ML field by providing its machine learning framework. Fundamentally, AML is a collection of tools and wizards to create highly sophisticated, intelligent, and high-end applications without tinkering with code.

AML provides predictions for your application using the power of easy-to-use APIs. To create new models, you have to carry out various operations, including regression, multi-class categorization, and binary classification. In such a scenario, AML can connect your applications to the cloud.

10. Accord.NET

It is an ML framework for scientific computing based on Dot Net technologies. It provides various libraries for different applications such as statistical data processing, image processing, linear algebra, and neural networks.

It has libraries, which are available as source code, installer, and NuGet packages. Its matrix library offers re-usability and slower algorithmic changes.


We have explored the top ten machine learning frameworks serving different arena of ML application development. Now, you have enough info to make the right choice for your ML app development needs. If you cannot fix it, we at SysBunny, invite you to discuss your Machine Learning application development project with our ML development team for end-to-end and cost-effective delivery.

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