Let’s go over the interfaces, libraries, and tools that are indispensable to the domain of Machine Learning. Here is the list of topics that this blog will cover along with the top 15 Machine Learning Frameworks:
What is a Machine Learning framework?
In its true sense, a Machine Learning framework is a collection of pre-built components that support the process of building Machine Learning models in a more efficient and optimized manner. It uses traditional methods and is very convenient for developers to use. As far as the computation process is concerned, these frameworks provide for parallelization. Good ML frameworks tackle the complexity of Machine Learning to make it more convenient and available for developers.
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Top 15 Machine Learning Frameworks
Today, we will take a look at the top 15 Machine Learning tools and frameworks that you can use to make ML modeling easier.
Amazon Machine Learning
Amazon Machine Learning is a cloud-based service that consists of visualization tools for developers with any level of skills. For predictions, Amazon ML uses simple APIs in applications. There is no need for custom code or any kind of infrastructure management for this. Amazon ML can run multiclass categorization, binary classification, or regression on the data stored in Amazon S3, Amazon Redshift, or RDS to create a model. There is no need for complex algorithms with Amazon ML.
Amazon ML can:
- Measure the quality of Machine Learning models through evaluation
- Carry out batch predictions and real-time predictions
- Generate predictions from the patterns in the input data using ML models
Apache SINGA is a distributed Deep Learning platform that was developed by the NUS Big Data Systems team. It comprises an open-source ML library with a scalable architecture that can run over a wide range of hardware, and due to its capability to support a number of Deep Learning models, SINGA allows users to customize the models. The programming model is quite simple that makes the distributed training process transparent to the users.
Training a Deep Learning model or submitting a job in SINGA requires users to configure the job with their own built-in layer, updater, etc., which is not the case in Hadoop.
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TensorFlow is an open-source library developed by Google Brain, which uses data flow graphs during numerical operations and performances. It comes with a rich set of tools and requires a sound knowledge of NumPy arrays. Batches of data called tensors are processed by a series of algorithms described by a graph that can be assembled with Python or C++. TensorFlow can run on both CPUs and GPUs.
TensorFlow is one of the most common Machine Learning frameworks. While it is simple enough to generate a prediction on a given dataset, it can also handle multiple data pipelines, the customization of all the layers and parameters of a model, data transformations to fit the model, training multiple machines without compromising user privacy, etc.
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Scikit-Learn is a free ML library and is a Python Machine Learning framework. It is designed to leverage Python’s numerical and scientific libraries, namely, NumPy, SciPy, and Matplotlib. It is open-source, reusable, and has tools for several ML tasks such as:
- Linear regression
- Support vector machines (SVMs)
- K-nearest neighbor
- Stochastic gradient descent models
- Decision tree and random forest regressions
SciKit can also assess the performance of a model with the help of tools like the confusion matrix. From Scikit-learn, users can always move to other frameworks seamlessly.
MLlib Spark is the ML library by Apache Spark, which includes common learning algorithms and utilities, along with the following:
- Higher-level pipeline APIs
- Dimensionality reduction
- Collaborative filtering
- Lower-level optimization primitives
As is the case with most Machine Learning frameworks, it aims to make practical Machine Learning convenient and scalable. MLlib has APIs in Java, Python, R, and Scala.
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Spark ML can handle large matrix multiplications. This is possible because it runs in clusters, and the calculations are done on different servers. Matrix multiplications require a distributed architecture for optimized speed and reduced memory issues while handling large datasets.
It is possible to use Spark ML with Spark SQL DataFrames, which is quite familiar to most Python programmers. Spark ML allows working with the Spark RDD data structure instead of NumPy arrays. This eliminates some complexity from data preparation for ML algorithms as it creates Spark feature vectors.
Keeping speed, modularity, and articulation in mind, Berkeley Vision and Learning Center (BVLC) and the community contributors came up with this Deep Learning framework called Caffe. Its speed makes it ideal for research experiments and production edge deployment. It comes with a BSD-authorized C++ library with a Python interface, and users can switch between CPU and GPU. Google’s DeepDream implements the Caffe framework. However, Caffe is observed to have a steep learning curve, and it is difficult to implement new layers with Caffe.
Torch has a fast scripting language and is very efficient. It aims to feature maximum flexibility, simplicity, and speed while users build scientific algorithms. It supports ML algorithms that prioritize GPUs and has an underlying C/CUDA implementation and LuaJIT.
Torch includes community-driven packages in Machine Learning, parallel processing, signal processing, computer vision, image, audio, video, and networking, and many more.
Keras is built on top of TensorFlow but is not limited to it. This makes modeling simple and straightforward. This neural network library can use the same code to run both on CPU and GPU. Some of the coding processes can be simplified with Keras.
Keras can be used with:
- Microsoft Cognitive Toolkit (CNTK)
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The ML framework, mlpack is C++ based and specifically designed to optimize speed, scalability, and use. There are 16 available repositories, and the implementation of this ML library can be carried out with command-line executables for novice users or with the C++ API for high performance and flexibility. The algorithms provided by this framework can be later integrated into large-scale solutions.
By using C++ templates, users can avoid copying datasets, and they work on expression optimizations that are not available in other languages.
Azure ML Studio
Azure users can build and train models using this Machine Learning framework. These models can be turned into APIs for use by other services. There is a 10 GB of storage per account for model data. However, any Azure storage can be connected for larger models.
Thanks to Microsoft and third parties, Azure ML Studio comes with a wide range of algorithms. There is no need for an account to try them out. You will get up to 8 hours of anonymous login.
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Google Cloud ML Engine
Google Cloud ML Engine aids Data Scientists and Developers to build and run superior ML models in production. It uses the distributed network of computers by Google. Google speeds up the process by running the algorithm on multiple computers. Its prediction and training services can be used both separately or together. Its applications come in the form of solutions for food safety, quick customer emails, presence of clouds in satellite images, etc.
Another benefit is that with Cloud ML Engine, the training data can be easily stored online in buckets in the Google Cloud Storage.
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Theano was developed at the LISA lab and was released under a BSD license as a Python library that rivals the speed of the hand-crafted implementations of C. It is especially good with muti-dimensional arrays and lets users optimize mathematical performances mostly in Deep Learning with efficient Machine Learning Algorithms. Theano uses GPUs and carries out symbolic differentiation efficiently.
Several popular packages such as Keras and TensorFlow are based on Theano. Unfortunately, Theano is now effectively discontinued but still considered a good resource in ML.
Veles is written in C++ and has its applications in Deep Learning. It is a distributed platform that implements Python for node automation and coordination. Its main focus is on flexibility and performance. Using Veles, one can analyze datasets and automatically normalize them before feeding them into the cluster. A REST API makes the trained model ready to be used for production immediately. Veles enables training of convolutional nets, recurrent nets, fully connected nets, and many more popular topologies.
Choosing Machine Learning Frameworks
Before choosing from these Machine Learning frameworks, turn your attention to the goal at hand: Machine Learning or Deep Learning.
Deep Learning requires neural networks to analyze a range of data through several tasks. The data could be:
- Numbered data
- Categorical data
- Image data
- Language data
Machine Learning relies on mathematical and statistics-based algorithms to find patterns. Keeping that in mind, you can look up tools that enable solutions, such as regression, k-mean clustering, neural networks, etc.
For choosing suitable Machine Learning frameworks, here are some of the best practices that are followed across the industry:
- Speed and storage consumption: Due to dynamic resource allocation, cloud-based models work faster. Model building, storage, or retraining does not consume user space because of the cloud usage. Distributed frameworks like Spark MLlib can reduce the model building time, but that adds in-memory infra cost.
- Infra cost and license cost: Python Scikit-Learn comes with a plethora of libraries. It is open-source, and users can save on the cost of the license. Google ML service charges minimal as well. However, using TensorFlow/Keras for deep neural networks can add to infra cost due to GPU.
- Initial exploratory data analysis: Open-source R or Python implements various libraries for the purpose of building graphs, data crunching, and cleaning.
- Big Data handling: Python is faster than R and hence more preferred. Tensor-based DL frameworks can seamlessly handle large data for model building. Spark MLlib’s distributed in-memory operation works well too.
- Rich source of libraries and complex algorithms: Open-source tools and frameworks have more libraries than cloud frameworks. A Python-based framework, along with TensorFlow or Keras, offers a range of ML modules, including AutoML. Google ML APIs work better for complex algorithms, such as image processing, word embedding, video processing, text-to-speech and speech-to-text conversions, etc.
- Model explanation and presentation: It is easier to present and explain conventional ML models, ensemble models, such as Gradient boosting or XGBoosting, and tree-based models, such as decision trees and random forest, unlike the neural network-based models, which are not transparent at all.
- Model consumption (exposing as API, Dockerization, and DB storage): By implementing the cloud infrastructure, Azure ML, Google, AWS, etc. can expose a model easily as an API. Python and R implement Flask and Shiny R, respectively, to deploy a model as an API.
- Scalability: When it comes to scalability, cloud-based models are obviously more suitable than on-premises models due to their dynamic resource allocation, but they come at a cost.
- Model retraining: H2O is known for its AutoML concept of choosing the algorithm, tuning hyperparameters automatically, and continuously learning from new data. Google ML and Scikit-Learn come with great AutoML features and libraries as well.
- Data security: SAS is preferred for its data security. Naturally, it finds its applications majorly in the banking domain.
- Popularity index: TensorFlow is the leading Deep Learning framework. Other widely popular ones include Keras, Caffe, and PyTorch. Scikit-Learn is the most used Machine Learning framework.
- Usage levels: Spark MLlib, TensorFlow, R, and Scikit-Learn are mostly popular among developers. On the other hand, Microsoft Azure ML, IBM SPSS, and Rattle are the best GUI-based options for professionals with only statistical knowledge. Experience with coding is not essential for these.
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Here concludes the top 15 Machine Learning frameworks list. These frameworks and tools not only democratize the algorithm development but also accelerate the process. Apart from the open-source community, large enterprises have started building their own frameworks.
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