Introduction to Machine Learning Frameworks
In the present world, almost every organization is making use of machine learning and artificial intelligence in order to stay ahead of the competition. With the help of these emerging technologies, many organizations are able to find smart solutions. A machine learning framework is a library, interface or any tool that is generally open source and enables the people to build various machine learning models with ease. People don’t even need the in-depth knowledge of the various machine learning algorithms as it contains pre-built libraries.
As it is a bit difficult for the companies to choose the best machine learning framework that is suitable for their use case, a better understanding of the various machine learning frameworks is important. So, let us see the most popular and best machine learning frameworks and their uses.
Popular Machine Learning Frameworks
Tensorflow is a machine learning framework that was developed by Google’s brain team and has a variety of features and benefits. It is an open source framework. It supports languages like Python and R and processes the data with the help of data flow graphs. This framework can perform classification, regression, etc., but performs very well with neural networks. The machine learning models developed by TensorFlow are simple to construct, capable of producing reliable results, and allow for effective experimentation in research.
TensorBoard, a large package that is typically overlooked, is included within TensorFlow and is used for data visualization. When working with shareholders, TensorBoard makes it easier to visually represent the data. Tensorflow’s application goes beyond simple training by supporting data pre-processing, feature engineering, and model serving. It can operate both on CPUs and GPUs.
- The computational graph visualizations provided by Tensorflow are excellent.
- Better performance and scalability.
- It has excellent community support.
- It is a bit slow when compared to its competitor frameworks.
- It’s hard to find errors in tensorflow due to its unique nature.
Keras is also a machine learning framework developed on top of Tensorflow. It is an open-source framework that is written in Python and can efficiently operate on both GPUs and CPUs. Keras can be used with the Microsoft Cognitive Toolkit, R, Theano, and plaidML. It is mainly used for deep learning applications. Various companies like Google, Uber, Facebook, and Netflix are using Keras to increase their scalability. It is developed by Francois Chollet, with over 300,000 users and 800 open-source contributors. Keras supports a high-level neural network API written in Python. The fact that Keras is built on top of TensorFlow, Theano, and CNTK makes it fascinating.
- It is user-friendly as it provides easy APIs and delivers helpful advice in the occurrence of user error.
- Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible.
- Keras is appropriate for advanced research because it is straightforward to add new modules and is thus easily expandable.
- It is simple and has pre trained models.
- It runs on top of Tensorflow, Microsoft CNTK and Theano.
- It lacks data pre-processing features compared to SKLearn.
- It is very difficult to find the errors.
PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. It is developed by Facebook’s AI Research Lab (FAIR) and authored by Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan. It is developed with the help of languages like Python, C++, and CUDA.
- Due to its hybrid front-end, it offers flexibility and speed.
- Allows for scalable distributed training and performance optimization in both research and production using the “torch distributed” backend.
- Deep Python integration makes it possible to easily create neural network layers in Python using well-known modules and packages.
- It is simple and easy to understand.
- It is a collection of powerful APIs.
- It is better than tensorflow as it follows a more pythonic approach and is object oriented.
- Low level computation cannot be handled by keras.
- Very difficult to find errors.
- Doesn;t have many visualization tools.
Caffe is abbreviated as “Convolutional Architecture for Fast Feature Embedding”, and is developed as a versatile, quick, and expressive deep learning framework. It is developed in collaboration with the general public and the Berkeley Vision & Learning Center (BVLC). Yangqing Jia created the concept while pursuing his doctorate at Berkeley. It provides an interface that lets programmers move between the CPU and GPU. Most of the organizations make use of Caffe in order to deal with computer vision and classification related problems.
- It allows us to solve the problem without a single line of code.
- It is very fast and supports GPU.
- Multiple GPU training is not fully supported.
- It is not a good when dealing with RNN (Recurrent Neural Networks)
CNTK is a deep learning framework that was created by Microsoft Research. It uses a direct graph to build a neural network through a series of computations. CNTK is used for facial, speech, and handwriting recognition and supports interfaces like Python and C++. It is well-known for its speed and efficiency, as well as its support for DNN, RNN, and CNN neural networks. It is an open source framework that has been available since April 2015. This can also be used as a library in our Python programming.
- It is flexible and deals well with RNN.
- It also allows distributed training.
- Source code is a little difficult to understand.
Theano is one of the fastest and simplest ML libraries, and it was built on top of NumPy. It was developed at the LISA lab to facilitate the quick creation of practical machine-learning algorithms. It was created using Python and CUDA and published under the BSD license. It is used by programmers to handle multidimensional arrays and gives users the option to optimize mathematical representations in ML applications. Theano works well with both GPU and CPU platforms. When used in GPU architectures, this framework can complete tasks 140 times faster. Theano is widely used in machine learning applications.
- It can perform well with both CPU and GPU.
- It automatically detects the error if any.
- It has built-in tools for validation.
- It is a bit slow when compared to Tensorflow.
- It doesn’t contain many pre-trained models.
Scikit-Learn, or simply called SKLearn, is the most popular machine learning framework that supports various algorithms for classification, regression, and clustering. It is very easy to implement and well documented. It also helps in model training and model building. It is one of the most commonly used frameworks for data mining and analysis in the current scenario. It also provides some of the model analysis tools, such as the confusion matrix, to check the performance of the model.
- It has various algorithms and even ensemble features that help in prediction of several ML models.
- Allows clustering of unstructured data.
- Not much used for deep learning.
H2O is an open-source machine learning framework with a business focus. Making decisions based on detailed data requires the use of predictive analytics and mathematics. It supports database independence and uses open-source Breed technology to teach robots using data insights. Java is the core code of H2O and the REST API makes it possible for any external source code or script to access or embed the H2O from external source code. It can be expanded by machine learning experts to interact with existing tools and programming languages. Machine learning applications where H2O can be used are customer segmentation, insurance, advertising technology, risk analysis, healthcare, fraud analysis, etc.
- It’s very efficient to perform auto ML along with H2O.
- It is simple and effective.
- Less scalable and adaptable.
Amazon Machine Learning
It is a cloud-based framework that allows developers to create various machine learning algorithms on the cloud. It consists of various visualization tools that help in making different insights from the data. The data that is stored in RDS or Amazon S3 can be fetched and used to develop various machine learning models. It is mainly used in classification problems, stock market price prediction, etc.
- It is cost effective.
- Performs well and supports multiple ML projects to support cloud platforms.
- Little difficult to learn.
- Too many APIs might lead to confusion.
Azure ML Studio
Azure ML Studio is a machine learning framework that helps developers to build different machine learning models as well as the APIs. It provides almost 10 GB of storage in order to store the models for a single account. Its widespread application lies in predictive modeling, enterprise-grade security, cost management & analysis, etc.
- It is cost-effective and user friendly.
- Better scalability and adaptability.
- Doesn’t support automated model training.
- It’s a bit complex.