If you are someone who has been exploring machine learning for a while, you have to understand the importance of choosing the right framework. There are many options available for that, and it is important for you to choose the framework that suits your project goals and experience level. This blog is all about the top 15 Machine Learning frameworks that you can use for your projects.
Table of Contents
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. During data processing, these frameworks helps the tasks to run in a parallel way, which means multiple operations can be done at a time. This helps to speed up the computation process, and the entire process becomes more efficient. Good Machine Learning frameworks tackle the complexity of Machine Learning to make it more convenient and available for developers.
Popular Machine Learning Frameworks
In this blog, we will take a look at the top 15 Machine Learning tools and frameworks that you can use to make ML modeling easier.
1. TensorFlow
TensorFlow is an open-source library, developed by Google Brain, that 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. Tensors, which are also known as groups of data, are handled in a step-by-step format by using a set of algorithms. These sets are organized like a flowchart, which is calles a computation graph. This graph can be built using programming languages like 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 data set, it can also handle multiple data pipelines, customization of all layers and parameters of a model, data transformations to fit the model, training multiple machines without compromising user privacy, etc.
1.1. Pros of Using TensorFlow
- It creates clear and detailed visual diagrams that show the flow of data through the different steps of computation.
- Since TensorFlow is supported by Google, it works efficiently, runs smoothly, and receives frequent updates. This helps to improve the performance of the model and add new features.
- It also helps in the execution of subparts of a graph. This enables machines to extract discrete data.
1.2. Cons of Using TensorFlow
- TensorFlow lacks in both speed and usage when compared with its competitors.
- TensorFlow can be complex for beginners, especially when you are dealing with low-level API, custom layers, or debugging model graphs. It may take time for new users to understand the structure and execution of the model.
2. Scikit-Learn
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. scikit-learn is open source, reusable, and has tools for several ML tasks such as:
- Linear regression
- Clustering
- Support vector machines (SVMs)
- K-nearest neighbor
- Stochastic gradient descent models
- Decision tree and random forest regressions
scikit-learn can also assess the performance of a model with the help of tools such as the confusion matrix. From scikit-learn, users can always move to other frameworks seamlessly.
2.1. Pros of Using Scikit Learn
- It allows the clustering of unlabeled data.
- It provides you with an ensemble feature that helps you to combine the predictions of several supervised models.
2.2. Cons of Using Scikit Learn
- It is also not suited for web applications. This is because it focuses mainly on data analysis and machine learning.
- It is not the best option for you if you want to build deep learning models, like the models that are used for speech recognition.
3. Keras
Keras is a free and open-source library that works on top of TensorFlow. It is scripted in Python and works well on both CPUs and GPUs. It was created by a Google engineer named Francois Chollet to make the process of building deep-learning models simple and fast. Today, it is used by many developers because it is easy to learn and it helps to build complex neural networks without too much effort. Keras is applicable in many areas, like healthcare, business forecasting, customer service, and even in virtual assistants.
Keras can be used with:
- R
- Theano
- Microsoft Cognitive Toolkit (CNTK)
- PlaidML
3.1. Pros of using Keras:
- It consists of a collection of pre-trained models.
- Keras is a user-friendly tool that helps you build deep learning models. It works well on different platforms like Windows, macOS, or Linux, and can connect to different systems that handle complex tasks in the background.
3.2. Cons of using Keras:
- Keras fails to handle low-level computation.
- It is not easy to identify the errors produced by the Keras framework.
4. PyTorch
PyTorch is easy-to-use machine learning and deep learning tools which was created by Facebook. It is built on top of another field called Torch, therefore it is named PyTorch. Developers used programming languages like Python, C++, and CUDA to build it. PyTorch is responsible for simplifying complex tasks, therefore it is very popular among beginners. It also works well with tools like PDB and PyCharm for debugging. PyTorch is widely applicable in areas such as computer vision, research, and NLP.
4.1. Pros of Using PyTorch:
- It is beginner-friendly.
- It supports mutliple GPUs.
- It works well with Python. This makes it easier to use for data science tasks.
4.2. Cons of Using PyTorch:
- It lacks monitoring and data visualization skills.
5. MLlib Spark
MLlib is a library in machine learning that comes with Apache Spark. It is an open-source library and consists of tools for building machine learning models, especially for those who already use Python. Unlike many other libraries, which use NumPy arrays, MLlib works with a special type of data structure in Spark called RDD (Resilient Distributed Dataset). This data structure is suitable for handling large amounts of data across multiple computers. Many common machine learning tasks are included in MLlib, like clustering, classification, and more.
As is the case with most ML frameworks, it aims to make practical Machine Learning convenient and scalable. MLlib has APIs in Java, Python, R, and Scala.
5.1. Pros of using MLlib
- It is very easy to use.
- It works with and supports multiple languages. This makes it easier for developers who use different types of languages to build machine learning models.
5.2. Cons of using MLlib
MLlib is not the ideal choice when there are multiple people working on the same project at the same time. This is because it does not support team collaboration features very well.
It does not improve or fine-tune models automatically during the training process. Therefore you have to adjust the settings manually to get better results.
6. Shogun
Shogun Machine Learning Toolbox is devoted to making machine learning tools available for free, to everyone. It provides efficient implementation of all standard ML algorithms. Shogun is written in C++ and it gets along perfectly with C++. Shogun ensures that the underlying algorithms are transparent and accessible—a unified interface provides access via many popular programming languages, including C++, Python, Octave, R, Java, Lua, C#, and Ruby. Shogun is used by developers for processing large-scale data for machine learning applications. It is used by ML developers in research, education, and NLP projects.
6.1. Pros of using Shogun
- Shogun turns out to be a good choice when you want to quickly try out new ideas or while building early versions of a project to see how they work.
- It provides users with flexibility to adjust features based on their needs and also allows them to control how the system behaves.
6.2. Cons of Shogun
- It does not have a popular community support when compared to TensorFlow or Caffe.
7. Caffe
Keeping speed, modularity, and articulation in mind, Berkeley Vision and Learning Center (BVLC) and community contributors came up with Caffe, a Deep Learning framework. Caffe is abbreviated as Convolutional Architecture for Fast Secure Embedding. 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 Caffe. However, Caffe is observed to have a steep learning curve, and it is also difficult to implement new layers with Caffe.
7.1. Pros of Using Caffe
- It works well on those devices that have limited processing power or memory.
- It is an open-source library and also very developer-friendly.
7.2. Cons of using Caffe
- It is not a good choice while you are dealing with sequence modeling or Recurrent Neural Networks (RNN).
- It has a very steep learning curve, which means that it can be difficult to understand for beginners or for those who are new to machine learning.
8. H2O
H2O is another open-source Machine Learning framework. It is business-oriented and implements predictive analytics and math to help drive decisions based on data and insights. This AI tool brings together unique features such as database-agnostic support for all common database and file types, easy-to-use WebUI and familiar interfaces, and the best open-source Breed technology. H2O comes with several models and includes Python, R, Java, JSON, Scala, JavaScript, and a web interface. H2O’s core code is in Java, and the REST API allows access from any external program or script to H2O’s capabilities. It allows users to work with existing languages and AI tools extend into Hadoop environments without any issues. H2O can be used in predictive modeling, advertising technology, healthcare, customer intelligence, risk and fraud analysis, insurance analytics, etc.
8.1. Pros of using H2O
- It is customizable.
- It is very helpful to use H2O in automated machine learning (AutoML) because it makes building and selecting of models easier and faster.
8.2. Cons of Using H2O
- The H2O framework does not scale properly. This means that it might struggle to handle very large datasets or complete models across multiple machines.
9. MLpack
ML Pack is a machine learning framework. It is built using C++ and is designed to be fast, scalable, and easy to use. It consists of 16 different modules or repositories that users can work with. If you are new to ML Pack, you can start using it through simple command-line tools.
One of the advantages of ML Pack is that it uses C++ templates. This helps it to work with data without the need to make extra copies. This helps to save memory and speed things up. Hence, ML Pack is considered a good choice while integrating Machine Learning into high-performance systems.
9.1 Pros of using ML Pack
- It provides you with a lot of tutorials and detailed guides. This makes it easier anf faster to learn and understand how to use this framework effectively.
10. Azure ML Studio
Azure users can build and train models by using these Machine Learning frameworks. This ML framework helps Microsoft users model ML projects seamlessly. This framework also allows developers to create APIs out of the ML models and use them on other devices. These models can be turned into APIs for use by other services. There is 10 GB of storage per account for model data. However, any Azure storage can be connected to 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 eight hours of anonymous login.
10.1. Pros of using Azure ML Studio
- For a larger cloud model, developers can connect to Azure cloud storage.
- It is scalable.
10.2. Cons of using Azure ML Studio
- It is not recommended to use it when you want to deploy your models outside Microsoft Azure.
11. Google Cloud ML Engine
Google Cloud ML Engine aids data scientists and developers in building and running superior ML models. It uses Google’s distributed network of computers. Google speeds up the process by running the algorithm on multiple computers. Cloud ML Engine’s prediction and training services can be used separately as well as together. Its applications come in the form of solutions for food safety, quick customer emails, the 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 Google Cloud Storage.
11.1. Pros of using Google Cloud ML Engine
- The ML cloud engine of Google is built on the powerful infrastructure of Google. This means it can handle large datasets and complex models easily.
- It provides smooth integration with tools like BigQuery, Google Cloud Storage, and TensorFlow. This helps you to build and deploy models more efficiently within the cloud ecosystem of Google.
11.2. Cons of using Google Cloud ML Engine
- Setting up and managing machine learning pipelines in Google Cloud ML can be a very complex task.
- The cost can be high when used heavily.
12. Theano
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 was built on top of NumPy and is one of the fastest ML libraries. Theano is especially good with multidimensional arrays and lets users optimize mathematical performanceperformances, 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 is still considered a good resource in ML.
12.1. Pros of Using Theano
- It can handle large amounts of data using either a CPU or a GPU. It all depends on what is supported on your system.
- It can avoid errors and bugs automatically while working with exponential and log functions.
- It comes with tools and helps you to test and check the working process of your ML models.
12.2. Cons of using Theano
- The compile time of Theano is comparatively slower then TensorFlow.
13. WEKA
WEKA is a collection of data mining and machine learning tools that you can easily use on your data. It is helpful with tasks like classification, pre-processing, clustering, and regression. It is often used in fields like healthcare, research, and projects that involve making decisions from data.
13.1. Pros of using WEKA
- This framework includes all the basic features required by a student to begin working on ML projects.
- It is easy to use for tasks like clustering and classification.
13.2. Cons of using WEKA
- It provides you with limited learning materials and online support.
14. Amazon Sagemaker
It is a cloud-based platform that helps developers build machine learning models. It consists of easy-to-use tools and visual interfaces that are suited for both beginners and experienced programmers. Developers can quickly connect to services like RDS or Amazon S3 and use them for model prediction. This platform is commonly used for tasks like image recognition, prediction of outcomes, and analyzing stock trends.
14.1. Pros of using Amazon Sagemaker
- It supports ML projects to leverage cloud infrastructure.
- It is cost-effective and also provides high performance.
14.2. Cons of using Amazon Sagemaker
- It still needs to improve in the way it works with different programming languages.
15. Veles
Veles is written in C++ and has its applications in Deep Learning. Veles is a distributed platform that implements Python for node automation and coordination. Veles’s main focus is on flexibility and performance. By using Veles, one can analyze data sets 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 the training of convolutional nets, recurrent nets, fully connected nets, and many more popular topologies.
15.1. Pros of using Veles
- Veles is designed for data analysis and machine learning computations.
- It also supports distributed computing. This means that it can handle large amounts of data across multiple machines.
15.2. Cons of using Veles
- It is harder for beginners to understand and use effectively.
- A very few number of developers use Veles. Therefore, it is hard to find help or tutorials.
Choosing Machine Learning Frameworks
Before you choose a machine learning framework, it is important to decide whether your goal is machine learning or deep learning. This will help you to choose the most appropriate framework.
Deep Learning requires neural networks to analyze a range of data through several tasks. The data could be:
- Language data
- Numbered data
- Categorical data
- Image 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 ML 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 cloud usage. Distributed frameworks, such as 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 as well. Google ML service has minimal charges. However, using TensorFlow or 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, is more preferred. Tensor-based DL frameworks can seamlessly handle big 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 algorithms, 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. This is because of cloud-based models’ 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.
- 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 frameworks 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, since coding experience is not essential for these, Microsoft Azure ML, IBM SPSS, and Rattle are the best GUI-based options for professionals with only statistical knowledge.
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Conclusion
The frameworks and tools listed in this blog not only democratize the algorithm development but also accelerate and simplify the process. In addition to the ML frameworks in the open source community, some of the large enterprises today, have also built their own frameworks for their in-house operations. Upskill with our Machine Learning training by mastering predictive modeling, evaluation metrics, TensorFlow, and Keras.
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