Best Machine Learning Frameworks(ML) for Experts in 2022

By | January 12, 2022
Machine Learning Frameworks

Machine learning (ML) focuses on providing machines with the ability to learn without being programmed and improve their decision-making capability over each iteration through experience. In other words, machine learning enables machines to learn just like humans.

ML is said to be a part of artificial intelligence or AI, and it has several real-world applications. For example, natural language processing (NLP), pattern recognition, image classification, clustering, and regression are all different applications of machine learning. Also, ML is extensively used for performing data science, and it helps businesses to perform predictive data analysis, make business decisions, and forecast the future.

In this article, we will be discussing the best machine learning frameworks that machine learning engineers and data scientists use to develop robust machine learning models. But before moving to that, let’s quickly overview the types of algorithms that machine learning uses.

Types of Machine Learning Algorithms

Machine learning can be performed through a set of algorithms. These algorithms can be divided into three categories:

1. Supervised Machine Learning: Supervised machine learning trains a machine by providing a set of data and the expected outcome, i.e. we already label the data. The machine builds a model based on the training data set. Once the model is built, another data set (testing data set) is fed to the machine to check the accuracy of the model.
Examples of supervised machine learning algorithms are classification algorithms, like Support Vector Machines, Decision tree, Linear classifier, and regression algorithms, like linear regression, polynomial regression.

Click here to learn more about supervised learning.

2. Unsupervised Machine Learning: This type of algorithm provides no training to a machine. The machine finds similarities and patterns in the data all by itself (without any form of supervision). Also, unsupervised learning is more complex than supervised learning.
Some examples of unsupervised machine learning algorithms are clustering algorithms, like K-means clustering, DBScan, dimensionality reduction, and Association rules, like Apriori, ECLAT.

Vamware

Click here to learn more about unsupervised learning.

3. Reinforcement Machine Learning: The third primary type of machine learning and the most complex of all is reinforcement learning. It involves an agent that has to take actions in a specific environment to ensure maximum rewards. The reward is cumulative and follows certain rules. Game theory, information theory, multi-agent systems, statistics, etc. use reinforcement learning.
Reinforcement machine learning is also referred to as neuro-dynamic or approximate dynamic programming. Markov Decision Process and Q learning are vital learning models in reinforcement learning.

Relationship between Artifical Intelligence, Machine Learning, and Deep Learning

Machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning. Thus, we can say that both machine learning and deep learning are subsets of artificial intelligence. You can understand the relation between the three from the below image: relation between machine learning and deep learning

Also, you can check out our AI vs ML vs Deep Learning article that highlights all the key differences between the three in detail.

Top 15 Machine Learning Frameworks

There are many machine learning frameworks out there, thus it will become difficult for you to find one that best suits your needs. However, to make the selection process easier for you, we have listed the best machine learning frameworks along with their brief overviews as follows:

1. TensorFlow

TensorFlow

TensorFlow is an open-source machine learning and deep learning framework developed by Google. Its libraries use graphs to describe workflows and complex networks in a simple manner. TensorFlow is used for both research projects and real-world applications.

The key highlight of TensorFlow is its ability to create neural networks. Also, TensorFlow works on all major operating systems, including Windows, macOS, and Linux.

TensorFlow is built using the C++ programming language, and it supports various programming languages, including Python, Java, and C. The framework is ideal to use for image recognition, image search, language translation, and speech recognition.

 

2. Keras

Keras

Keras is a deep learning framework that has broad community support. It is written in Python and supports many neural-network computation engines, especially Convolutional Neural Networks. Keras is used along with low-level computation libraries like TensorFlow as a high-level API. Also, it comes with an interface that is easier to use as compared to TensorFlow. The other ‘Backend’ engines/libraries that support Keras are Theano & CNTK.

Developers can create new modules as a set of classes and functions using Python. The existing stand-alone modules can be used to create new models. With Keras, code deployment is fast, and it supports many devices, including Raspberry Pi. Moreover, you can use Keras with single or multiple GPUs at the same time.

The base of Keras is ‘Model’ which defines the whole network graph. You can use the same model, or add more layers to it to suit your requirements. You can build a simple sequential model with the following steps:

  • Creating a sequential model
  • Convolution layer
  • MaxPooling layer
  • Dense layer
  • Dropout layer
  • Network Compilation, training, and evaluation

Keras is used for face recognition, computer vision, and many other use cases that require deep neural networks.

3. Scikit-learn

Scikit-Learn

Perhaps the most widely used and useful general-purpose machine learning framework, Scikit-learn contains all the tools for statistics, for example, classification, regression, dimensionality reduction, clustering, etc. It is free and contains almost all the popular algorithms, like Naïve Bayes, Decision tree, KNN, SVM, etc.

Scikit-learn is written in Python, C, and C++ and built on top of the SciPy ML library. Some of the key features of Scikit-learn are:

  • Free, open-source, and commercially usable (BSD license).
  • Along with ML algorithms, it also has pre-processing, cross-validation, and visualization algorithms.
  • Excellent documentation and community support.

Scikit-learn is more like a high-level API for building standard machine learning models. Unlike TensorFlow, it doesn’t come with an automatic differentiation toolkit. Also, Scikit-learn is apt for building quick algorithms, whereas TensorFlow requires more training time, thus making it more suitable for deep learning networks.

4. H2O.ai

h2o

H2O.ai is an open-source platform that aims to make ML easier for business users. Business users need not worry about deployment, performance tuning, and other low-level tasks, and they can gain insights from data quickly.

H2O.ai is a suite of products with the most popular ones mentioned below:

  • H2O It is an open-source, distributed, and in-memory platform for ML and predictive analysis. It can build production-ready models for supervised and unsupervised algorithms, like classification and clustering. H2O has a simple yet effective UI known as flow.
  • Steam – This is an enterprise offering where data scientists can build, train and deploy ML models that users can access by various applications via APIs.
  • Driverless AI – Though named so, it is not specific to autonomous driving but also performs automatic feature engineering, model selection, tuning and ensembles, and helps in calibrating parameters that optimize the algorithms for specific use-cases.

H2O also provides REST API for external programs to access its capabilities over HTTP via JSON. H2O also supports algorithms other than supervised and unsupervised algorithms, like the quantile, early stopping, Word2Vec.

5. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit

Abbreviated as CNTK, Microsoft Cognitive toolkit is an open-source kit for commercial level distributed deep learning. It is fast, scalable, and reliable. Microsoft products like Skype, Cortana, Bing, etc. use CNTK to develop commercial-grade systems using sophisticated algorithms.

It offers built-in components like CNN, RNN, FFN, reinforcement learning, supervised and unsupervised learning, LSTM, generative adversarial networks, and many more. Developers can add their custom components using Python on the GPU.

Microsoft Cognitive Toolkit offers automatic hyperparameter tuning. Also, the GPU Memory can fit large models and still maintain speed, efficiency, and parallel processing.

The toolkit offers both high-level and low-level APIs to suit different business needs. You can evaluate the models using C++, C#, BrainScript, and Python. You can also train and host the models on the Azure cloud and even perform real-time training.

6. MXNet

MXNet

MXNet is a dynamic and flexible library for deep learning. It is short for mix and maximizes. It supports many programming languages, including Python, R, C++, Java, JavaScript, Julia, Perl, and MATLAB. The library is portable and scalable to multiple GPUs and machines.

Both MS Azure and AWS cloud providers support MXNet. Due to its ability to distribute processing into multiple GPUs, MXNet offers high speed and performance.

MXNet supports many tools and libraries to build applications in the field of computer vision, natural language processing, time series, and many more. Also, it supports automatic differentiation, i.e. automatically calculates the gradients required for training a model.

MXNet has a hybrid front end, i.e. the Gluon Python API, that lets developers switch to a symbolic mode for higher performance and faster execution.

7. Apple’s Core ML

Apple’s Core ML

Core ML comes as one of the most important ML frameworks for iOS and other operating systems used by Apple devices, like macOS, watchOS, and tvOS. It can produce real-time results with low latency. This is especially useful for the real-time prediction of live videos (successive frames) or images on the device. The application can run in offline mode (without network or API calls) without any loss of data.

Some of the popular applications of Core ML are real-time image recognition, face detection, text prediction, and speaker identification. The machine learning models are pre-trained on the cloud and then changed to Core ML format, which can be directly added to the required Xcode project. Some frameworks that support Core ML are Vision, GamePlaykit, and NLP.

8. Apache Mahout

Apache Mahout

Mahout is an ideal machine learning framework for data scientists, mathematicians, and statisticians who want to design algorithms using the out-of-the-box distributed backend. It is a linear algebra framework and contains libraries in Java/Scala for all the maths operations required for machine learning.

Apache Mahout has implementations for many algorithms, like clustering, classification, and collaborative filtering using the map/reduce paradigm. However, the framework is not restricted to only Hadoop-based implementations and also supports non-Hadoop clusters.

Following are the key highlight of Mahout:

  • It is suitable for performing data mining tasks on huge volumes of data.
  • It allows you to analyze large datasets in a short time.
  • The clustering implementations are based on map-reduce – for example, mean-shift, k-means, canopy, Dirichlet, etc.
  • Mahout offers in-built libraries for vectors and matrix operations.
  • It supports both complementary and distributed Naïve Bayes classification implementations

Many companies use Mahout for mining and modeling purposes. For example, Twitter uses it for interest modeling. Mahout algorithms have applications in many areas, such as game development, data mining, pattern recognition, expert systems, and robotics.

9. Massive Online Analysis

Massive Online Analysis

MOA is a framework that can build and run data mining and machine learning experiments on evolving data streams. It is free and open-source, and it contains almost all the popular machine learning algorithms, like Bayesian classifiers, Decision tree, bagging, boosting, perceptron, regression, clustering, recommender system, and pattern mining, etc.

The framework comes with a set of stream generators and learners that you can use on the command line, Java API as well as GUI.

MOA is especially suitable for real-time data streams and situations prone to concept drift, such as weather prediction, fraud detection, or shopping preferences of a customer through change detection algorithms. It is a well-suited framework for large-scale machine learning. The framework models concept drift (i.e. change in the statistical value of the target variable, leading to inaccuracy in predictions) as a weighted union of two pure distributions that characterize the target variable (concept) before and after the change (drift).

Datastream environment is a more challenging environment when compared to the traditional batch learning environment because a task can be processed only once within a limited time and using limited memory. The algorithm may need to perform prediction at any stage.

MOA is written in Java, making it portable and platform-independent. It is also related to WEKA, which has implementations for many algorithms so that MOA classifiers and streams can be used from WEKA and WEKA classifiers can be called from MOA.

10. Caffe

Caffe

Caffe is an expressive deep learning framework made for speed and modularity. It was earlier available under the BSD 2-Clause license, however, Facebook open-sourced it in 2017.

Interestingly, Caffe was integrated with Apache Spark to produce a distributed deep learning framework, namely CaffeOnSpark. It is written in C++ with Python and MATLAB programming interfaces.

Caffe is much suitable for industry deployment, like vision, speech, etc. as well as research projects. It can process more than 60 million images in a day on a single GPU, which is quite fast.

Caffe is suitable for major deep learning networks like Convolutional Neural Networks, Long Short-term memory, Long-term Recurrent Convolutional networks, and fully connected neural networks. Caffe stores data in the form of blobs. Also, the properties of the blob define how the information gets stored across various neural network layers.

Caffe is most suitable for mobile devices and computationally constrained platforms.

Bonus Machine Learning Frameworks

11. Azure ML Studio

Azure ML Studio

Azure ML Studio is a robust machine learning platform, especially for small and medium-sized businesses. It provides machine learning as a web service on a pay-as-you-go basis. This means that businesses can just purchase the services and immediately build their machine learning applications.

Due to its drag and drop components, there is minimal code required, and the interface becomes easy and flexible to use. Further, the configuration of properties is simple. Azure ML studio works well for massive data sets and supports most ML algorithms, such as classification, regression, and clustering. A typical implementation of web service follows the steps as shown below:

Azure ML Studio

Apart from the flexibility and extensibility, ML studio also offers excellent community support and documentation. If the out-of-the-box algorithms are not sufficient, developers can add their implementations in R or Python. Azure ML studio is great for both descriptive and predictive analysis, and it can help to accomplish all the steps of data science, ranging from data collection to evaluation of the model.

12. Amazon Machine learning

Amazon Machine learning

Amazon ML is a comprehensive service that uses a set of products consisting of algorithms and mathematical models for developing predictive applications. The service reads data through Redshift, RDS, and Amazon S3 and uses the Amazon ML API and AWS management console to visualize the data. Also, it allows you to use the S3 buckets to import and export data to other AWS services.

You can use Amazon ML for various algorithms that perform classification, dimensionality reduction, regression, and reinforcement learning (through Amazon SageMaker RL). Also, it is suitable for those working with other AWS services and just starting with ML because the choice of algorithms is limited (as of now). The main algorithm (model) for which AWS ML was built is the Stochastic Gradient Descent (SGD).

AWS ML is becoming popular because it is easy to learn and implement, and it also saves a lot of development time. AWS optimized TensorFlow framework can achieve near-linear scalability and better utilization of GPU by improving the way tasks are distributed across GPUs.

13. Theano

theano

Theano is an open-source project written in Python and CUDA. It contains a compiler for manipulating and evaluating mathematical expressions, particularly matrix-based expressions (n-dimensional arrays). Theano is built on top of the NumPy library.

Following are the key features of Theano:

  • It can handle complex computations required for large neural network algorithms.
  • Theano supports both convolutional and recurrent neural networks.
  • It is extremely fast, even for data-intensive computations (About 140 times faster than CPU), through the use of GPU.
  • Allows dynamic code generation for faster evaluation of expressions like differentiation, matrix arithmetic, gradient computation, etc.
  • It comes with tools for unit testing, debugging, and verifying bugs and potential issues.
  • Offers tight integration with NumPy.
  • Theano is also capable of performing differentiation (computing derivatives) efficiently.
  • It provides stable output expressions for large datasets

Theano builds an entire computation graph for the model and compiles the same into a highly efficient code by applying optimization techniques. A special operation, known as ‘function’, then pushes the compiled code into the Theano runtime. The ‘function’ is iteratively executed to train the neural network. One can visualize a computation graph using the printing package in the Theano library.

14. Shogun

Shogun

The Shogun ML toolbox is an open-source software written in C++. It offers a wide range of algorithms and data structures for different machine learning problems. The framework also provides interfaces for Python, R, Java, Ruby, MATLAB, C#, and Octave.

Shogun was mainly created for kernel-based algorithms like Support Vector Machines that can be used for both classification and regression. It provides a generic interface to 15 SVM implementations, like SVMLin, SVMlight, GPDT, LibSVM, LibLinear etc. Also, you can combine SVMs with over 35 different kernel functions.

Apart from kernel functions, Shogun also supports dimensionality reduction algorithms, like PCA, multidimensional scaling, diffusion maps, Laplacian Eigenmaps, etc., online learning algorithms, clustering algorithms, like k-Means and GMM, hidden Markov models, linear discriminant analysis, perception, and many more.

Shogun provides an implementation for many kernels like gaussian, polynomial, sigmoid, or linear kernels for numerical data, and weighted degree, spectrum, and weighted degree with shifts for a special type of data. An example of special data could be DNA sequence.

15. Torch

Torch

Torch is a popular scientific computing framework that uses GPUs for computing. It uses LuaJIT scripting language with C/CUDA underlying implementation. Torch makes scientific computing easy and offers high flexibility.

It has vast community support, and there are many dedicated packages for image networking, parallel processing, computer vision, signal processing, audio, and video networking, etc. Torch provides a lot of routines for slicing, indexing, resizing, transposing, and other data processing methods.

Torch is open-source, and the core package is known as ‘torch’. It supports all major mathematical operations, like the dot product, matrix-vector product, matrix-matrix multiplication, and statistical distributions, such as normal, uniform, multinomial.

The nn package in Torch is used for neural networks. It supports both feedforward and backpropagation operations using the respective methods. PyTorch is a Python implementation of the Torch framework, which provides dynamic graphs, fast computation, and a host of libraries for machine learning tasks.

Facebook extensively uses Torch for its AI development. IBM, Twitter, Idiap, Google, Nvidia, and Yandex also use Torch for research purposes.

16. Google Cloud ML Engine

Not to be confused with an ML framework, Google’s cloud ML engine is a platform that can perform training and predictions. You can use the service to train a model using ML frameworks, like Keras, XGBoost, Scikit-learn, TensorFlow, etc., or make predictions, or do both. Also, you can host a trained model on various other environments, including public cloud, external environments, and on-premises infrastructure.

The Google Cloud ML Engine can also perform hyperparameter tuning that evaluates the accuracy of results. Google Cloud ML engine takes in already prepared data into its Google Cloud storage bucket. It then initiates distributed training jobs that take in the data from the bucket. You can prepare data using other Google services available on Google Cloud itself, or you can upload external data directly to the Google Cloud.

How to Choose the Best Machine Learning Framework?

While picking a machine learning framework,  you need to figure out your business use case. As we have seen above, each ML framework has its unique strengths and can serve different purposes. For example, for building quick algorithms and models, you can use a general-purpose framework like Scikit-learn. On the other hand, for more complex calculations and data-intensive operations, Theano or TensorFlow are the best options. If you have less time to build a deep learning network, you can use Azure ML studio.

Conclusion

In this article, we have learned about the most important and widely used machine learning frameworks. These frameworks can significantly simplify your code so you can focus more on business logic. Among the frameworks listed above, TensorFlow, Caffe, CNTK are some brilliant frameworks for deep learning while frameworks like Scikit-learn and MOA are great for generic machine learning. Also, you can use Google Cloud ML, Amazon ML, and Azure ML Studio if you want to create ML models on the cloud.

Nonetheless, it’s quite important that you keep all your personal preferences and project requirements in mind so that you can choose the best machine learning framework.

If you have any suggestions or queries, feel free to share them with us in the comments section below.

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