Machine Learning Software Tools

Machine learning, as a subset of artificial intelligence, is usually based on specific software and applications. Today, we are going to take a closer look at this question and find out what kind of software is most commonly used in machine learning. For starters, we have good news! The vast majority of these systems are open source and free!

Machine learning is, essentially, based on teaching machines and algorithms how they should work. There are several most common goals of machine learning algorithms, and these are, i.a:

• Finding patterns in data (it helps in analyzing and explaining data your company possess)
• Detecting anomalies (it’s all about the identification of rare items, events or observations in data)
• Predicting future (ML algorithms, based on historical data, can help you assess the future sales level or market prices)
• Improving the decision-making process (the technique called the decision tree helps in making decisions and is commonly used in operations research, business intelligence, and strategic planning)

You may also find it interesting – Machine Learning and AI – Comparison.

Depending on your goal and the outcome you expect to receive, you should use various platforms or types of software. Sometimes it’s necessary to build one from scratch, especially when you want to build an advanced ML platform or project. In many other situations, ready-made solutions should be sufficient. Given that your selection is very wide, you should be able to find machine learning software that meets your needs.

That said, let’s analyze some of the most popular types of machine learning software, and check what can they be used for.

Machine learning software tools

We have a list of thirteen different machine learning software tools. Most of them are free or have a least one free plan. Moreover, there are supportive communities gathered around these platforms, so if you need any assistance with your projects–you’ll get it very quickly!

1. Machine Learning Software Tools: TensorFlow (open source)

It’s an open-source machine learning platform, one of the most popular solutions in the ML world. You can use TensorFlow to create and train new machine learning models and deploy existing models.

Thanks to a flexible scheme of tools, a multitude of libraries and other resources, this platform is a perfect fit, whether you’re an ML rookie or an experienced specialist.

Key features:

• Helps you along the way
• Offers good support of deep neural networks and machine learning
• You can build and train multiple ML models
• It’s flexible, adjusted to various levels of knowledge
• A cloud-based solution

2. AWS: Amazon machine learning platform (open source)

It’s a cloud-based and robust machine learning software provided by AWS, allowing you to build, train, and deploy your ML models. What’s particularly important, you can use their pre-trained AI services, what comes in handy when working with computer vision, language, recommendations, and forecasting. One of AWS platforms is Amazon SageMaker, which allows you to build ML models of any scale. It can be used for implementing ML across many use cases: Data analysis, prediction models, and classification.

Key features:

• Has pre-trained AI services
• Can be used for small and large projects alike
• Supports many types of models, i.a. classification and regression
• Offers broad framework support
• Has ML and AI services alike

3. Machine Learning Software Tools: Scikit-learn (open source)

This platform is specifically designed for ML projects written in the Python programming language. It’s an open-source library that supports both supervised and unsupervised learning models. Scikit-learn provides tools for model fitting, data preprocessing, model selection and evaluation, and many other ML utilities. It’s especially helpful when it comes to predictive data analysis.

Key features:

• Full documentation provided
• It’s commercially usable (has BSD license, imposing minimal restrictions on the use and distribution of covered software[1])
• Built on NumPy, SciPy, and matplotlib
• It can be used for regression, classification, clustering, dimensionality reduction, model selection, and preprocessing.

4. Machine Learning Software Tools: Pytorch (open source)

It’s a library for Python programs that facilitates building deep learning projects. It’s Torch-based, Python machine learning library (hence its name). It is primarily used for applications such as computer vision and natural language processing. It’s released on a modified BSD license so that you can use it in commercial projects.

Key features:

• It can be used in the cloud on locally on your computer (works on MAC, Linux, and Windows)
• Has a rich ecosystem of tools and libraries
• Provides a variety of optimization algorithms
• Easy to use and compatible with Scikit-Learn

5. Machine Learning Software Tools: Google Cloud Platform (paid)

Google AI Platform allows you to take your ML projects from ideation to production and deployment, quickly and cost-effectively (Google Cloud Platform has a free trial plan). This platform has an integrated toolchain that helps you build and run your own machine learning applications. Moreover, it gives you access to other technologies like TensorFlow, TPUs, and TFX tools.

Key features:

• Offers AI building blocks for less experienced users
• This platform provides the tools to evaluate the accuracy and tune hyperparameters of the build models
• Gives access to knowledge, research, tools, datasets, and other resources
• Can be used for forecasting, personalization, workflow automation, translation, speech recognition, and many other applications

6. Accord.NET (open-source)

The Accord.NET Framework is a .NET ML framework combined with audio and image processing libraries. It is used for building production-grade computer vision, computer audition, signal processing, and statistics applications. What’s important, it can be used for commercial purposes. This framework can be used for various machine learning models, especially classification, regression, and clustering. It also works when it comes to distributions and hypothesis tests.

Key features:

• Has a comprehensive set of sample applications
• Comes with extensive documentation
• A large, supportive community gathered on Stackoverflow.com
• Has very well-commented source code and well-established codebase

7. Shogun (open-source)

It’s a free machine learning library, developed in 1999. Although it’s written in the C++ programming language, it supports many other languages like R, Python, Java, Octave, C#, Ruby, Lua, etc. Shogun offers algorithms and data structures for various machine learning projects.

Key features:

• It’s focused on kernel machines, so it’s helpful to support vector machines for regression and classification problems.
• Capable of processing huge datasets consisting of up to 10 million samples.
• Licensed under the terms of the GNU General Public License

8. Keras.io (open-source)

Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. Keras is a central part of the tightly-connected TensorFlow 2.0 ecosystem, covering every step of the machine learning workflow, from data management to training and deployment. Keras is mainly used for deep learning projects.

Key features:

• Keras models can be exported to JavaScript to run directly in the internet browser
• It supports convolution networks
• Can be run on the CPU and GPU
• Comes with extensive documentation and developer guides

9. Google Colab (open-source)

It’s an online, open-source, browser-based platform. A free online cloud-based Jupyter notebook environment that allows you to train machine learning and deep learning models on CPUs, GPUs, and TPUs[2].

Thanks to Google Colab you can train machine models for free. This cloud-based service helps in building machine learning applications using the libraries of PyTorch, Keras, TensorFlow, and OpenCV.

Key features:

• Allows you to work with large datasets, build sophisticated ML models, and share work with others
• Offers three types of runtime for Jupyter notebooks: CPUs, GPUs, and TPUs
• Gives 12 hours of continuous execution time

10. Knime (open-source)

KNIME is a tool used primarily for data analytics. It’s an open-source solution designed for discovering the potential hidden in data, mining for insights, and predicting future. It allows users to create data flows, selectively execute analysis steps, and inspect the results[3]. KNIME comes with a range of commercial extensions.

Key features:

• The installation of KNIME is straightforward
• Comes with extensive documentation, comprising their analytics platform, server, extensions, and integrations
• It can be used for data analysis and business intelligence
• Has limited visualization and exporting capabilities

11. Weka (open-source)

This platform was designed especially for data mining purposes. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as Scikit-Learn[4]. What’s particularly interesting, Weka can be used to build machine learning pipelines, train classifiers, and run evaluations without the need to write a single line of code!

Key features[5]:

• Available under the GNU General Public License
• Runs on many platforms
• Contains a collection of visualization tools and algorithms
• Has a comprehensive collection of data preprocessing and modeling techniques
• Helps in data preprocessing, clustering, classification, regression, visualization, and feature selection

12. Apache Spark MLlib(open-source)

MLlib is Spark’s machine learning library consisting of common algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, and underlying optimization primitives. It runs on runs on Apache Mesos, Hadoop, Kubernetes, standalone, or in the cloud.

Key features[6]:

• You can access data from multiple data sources
• Comprises a wide array of algorithms
• Uses the linear algebra package Breeze
• Usable in Java, Scala, Python, and R

13. Core ML

Core ML made by Apple is an ML-based framework that helps you to integrate machine learning models into mobile apps. It is the foundation for domain-specific frameworks and functionality. Core ML supports computer vision for analyzing images, natural language processing for texts, speech recognition, and sound analysis.

Key features[7]:

• It builds on top of low-level primitives
• Provides a unified representation for all models
• Runs a model strictly on the user’s device, which removes the need for a network connection
• Used across Apple products, including Siri, Camera, and QuickType

Machine learning software tools – conclusion

As you can see, ready-made machine learning software is a wide subject. There are many applications and platforms designed for various ML endeavors. In many cases, software available on the market is fully sufficient, especially for less complicated projects.

If you’d like to conduct something more complex – we are for you! Addepto will help you choose the best commercially available platform, or even design a new one entirely from scratch. With our assistance, no machine learning project is too complicated to be executed!

See our machine learning solutions to find out more.

References

[1] Wikipedia. BSD licenses. URL: https://en.wikipedia.org/wiki/BSD_licenses. Accessed Jun 12, 2020.

[2] Abhishek Sharma. Free GPUs for Everyone! Get Started with Google Colab for Machine Learning and Deep Learning. Mar 23, 2020. URL: https://www.analyticsvidhya.com/blog/2020/03/google-colab-machine-learning-deep-learning/. Accessed Jun 12, 2020.

[3] Wikipedia. KNIME. URL: https://en.wikipedia.org/wiki/KNIME. Accessed Jun 12, 2020.

[4] Waikato. WEKA – The workbench for machine learning. URL: https://www.cs.waikato.ac.nz/ml/weka/. Accessed Jun 12, 2020.

[5] Dr. Sudhir B. Jagtap, Dr. Kodge B. G., Census Data Mining and Data Analysis using WEKA. 2013. URL: https://arxiv.org/ftp/arxiv/papers/1310/1310.4647.pdf. Accessed Jun 12, 2020.

[6] Apache. Apache Spark MLlib. URL: https://spark.apache.org/mllib/. Accessed Jun 12, 2020.

[7] Apple. Core ML. URL: https://developer.apple.com/documentation/coreml. Accessed Jun 12, 2020.