Artificial intelligence and machine learning have advanced very quickly over the past several years and are becoming widely used for a wide variety of purposes. The demand from businesses, educational institutions, and other customers continues to grow, and more and more sophisticated applications are developed every year. Of course, this wouldn’t be possible without powerful machine-learning software that allows for the creation of such applications in the first place.

There are a lot of different machine learning methodologies and consequently many competing software platforms that can harness the analytical power of those complex models. Since this is a highly dynamic field, even people who are actively engaged with projects based on machine learning are sometimes unsure which software would serve them the best.

The right answer to the question of which is the best machine learning software may depend on the type of tasks that will be expected from it. We will try to review the available platforms and explain in which situations they could be the best solution.

What is Machine Learning Software and Why Do You Need it?

Machine learning involves complex algorithms and huge training datasets, which are necessary in order for the software to develop expertise for certain tasks. Because the backend process is so technically complex and computationally demanding, end users typically interact with the software of this kind through specially designed applications. There are numerous platforms that let developers create custom deployments based on existing machine learning infrastructure or allow users to solve practical problems. Those services represent an important layer of the AI-driven ecosystem and open a whole world of new possibilities waiting to be explored. Some of those services include:

• Training of existing machine learning models with additional datasets
• Altering the models and testing new variations
• Labeling data used for model training
• Developing AI-powered applications for end users
• Conducting large-scale data analysis
• Predicting future events within a specific context (i.e. market movements)

Top 15 Machine Learning Software

Mahout

If your primary goal is to quickly develop machine learning applications that perform across the entire spectrum of users, Apache Mahout deserves a good look. It’s intended primarily for mathematicians and data scientists who need unique research tools based on machine learning, but its applicability is not limited to these professionals.

Main capabilities:

• Easily extensible program that allows the construction of custom algorithms
• The solution is based on a distributed linear algebra framework
• It comes with a large number of pre-made algorithms
• The software includes a vector math experimentation environment

Chief limitations:

• Advanced knowledge of machine learning is required
• Computation times are relatively slow compared to similar platforms

Price: free

User ratings:

• G2: 4.2 (13 reviews)
• Capterra: N/A

Oryx

Ambitious users interested in real time machine learning deployments might want to become more familiar with the Oryx platform. This amazing tool supports very large projects and provides developers with all the resources they need to unlock powerful functionalities. Oryx 2 platform is currently available, bringing tangible improvements over the original edition.

Main capabilities:

• End-to-end machine learning solution based on a generic lambda architecture
• It includes several layers, including a batch layer, a speed layer, a serving layer, and a data transport layer
• Machine learning tier can be simply deployed over an existing infrastructure
• Collaborative filtering allows for any part of the algorithm to be modified

Chief limitations:

• Too complex and demanding for applications of a smaller size

Price: free to download

User ratings:

• G2: N/A
• Capterra: N/A

TensorFlow

It’s generally accepted in the developer community that TensorFlow is one of the best environments to play around with advanced machine learning concepts. This is a Google product intended for deep learning and predictive modeling that supports researchers in every stage of the project, from initial data probing to verification of the findings.

Main capabilities:

• Machine learning platform that allows utilization of existing models or training of new ones
• Creates solutions suitable for server-side or client-side deployment
• Achieves excellent results in computer vision and natural language processing fields
• There are very good tutorials and other resources for new users

Chief limitations:

• No native support for Decision forests methodology is provided
• Project documentation is not always 100% consistent

Price: free

User ratings:

• G2: 4.5 (63 reviews)
• Capterra: 4.6 (99 reviews)

Amazon Forecast

Users focused on leveraging the business intelligence potentials of deep learning models have strong incentives to try this software. This is a fully managed online solution developed by a company with world-leading expertise when it comes to evaluation of market data and prediction of the future trends, so it may help to fuel growth in many industries.

Main capabilities:

• Market forecasting tool that utilizes big data and machine learning to produce accurate insights
• Uses historical data from the user to create customized predictions
• Very accurate on all levels from granular information to huge inventory optimization
• Recurring workflows can be deployed with no coding knowledge

Chief limitations:

• Active internet connection is required at all times
• More expensive licensing than for some of the alternatives

Price: Free tier is available, advanced deployments are charged per delivered service

User ratings:

• G2: 4.1 (34 reviews)
• Capterra: N/A

PyTorch

This is a very popular open-source platform that facilitates quick development of customized machine learning applications for many different purposes. It comes equipped with a wide range of libraries and tools that greatly simplify the process and open up practically unlimited possibilities. It can be used alongside C++ or Python programming languages.

Main capabilities:

• Distributed solution for machine learning algorithm training and optimization
• Numerous libraries are available to help new projects
• Designed to provide the quickest route from a prototype to a finished solution
• Models can be exported to other environments in the standard ONNX format

Chief limitations:

• This is not an adequate solution when working with small training datasets
• Relatively weak community support for error solving

Price: free (open-source)

User ratings:

• G2: 4.5 (13 reviews)
• Capterra: N/A

Weka

Some of the resources offered by this open-source platform can be instrumental for creating highly efficient data mining algorithms. Weka (Waikato Environment for Knowledge Analysis) is compatible with many different deep learning methodologies, and can be utilized for easier creation of innovative models. This software is made with the Java language.

Main capabilities:

• Collection of machine learning algorithms and tools for data mining
• It’s very useful for data-preprocessing and visualization
• Custom datasets can be plugged directly into an existing algorithm
• Educational resources about machine learning are provided

Chief limitations:

• User interface is quite primitive and unappealing
• Large datasets can be slow to upload

Price: free with GNU license

User ratings:

• G2: 4.3 (13 reviews)
• Capterra: 4.4 (14 reviews)

Scikit-learn

This software framework includes numerous algorithms that can perform various machine learning operations, such as Random Forests, Support Vector Machines, etc. Open-source nature of the project contributes to its accessibility, while new libraries are added all the time. For anyone actively involved in a machine learning project, scikit-learn is an essential asset.

Main capabilities:

• Library of algorithms for Python programming language suitable for classification, regression, and clustering tasks
• User-friendly interface that makes the resources available to all users
• Detailed documentation is provided for all of the algorithms

Chief limitations:

• No deep-learning algorithms are included in the collection
• It doesn’t handle categorical attributes as well as other software tools

Price: Free for commercial use with BSD license

User ratings:

• G2: 4.8 (56 reviews)
• Capterra: N/A

Izenda Business Intelligence

Some of the most impressive machine learning platforms are designed specifically to deliver business insights that can impact profits. With this powerful and highly customizable solution, corporate users can take advantage of the analytic capacity of modern AI technologies. Thanks to a smart interface, this BI solution can be easily controlled by non-coders.

Main capabilities:

• Enterprise-grade business intelligence platform for deploying web applications
• Excellent data visualization and reporting tools
• Highly responsive design aimed at instant availability of data across the organization
• Customizable self-service dashboards can be created within individual applications

Chief limitations:

• This solution is prohibitively expensive for smaller companies
• Narrow focus on a single use case for machine learning

Price: Free trial is available, full deployment starts at $12,000 per year

User ratings:

• G2: N/A
• Capterra: 4.4 (31 reviews)

SimpleAI

Machine learning researchers can solve numerous problems and develop new ideas by relying on the algorithms included in this framework. SimpleAI is a Python-oriented library of algorithms that primarily deal with tasks related to searching and classification. You can be certain that all code found here will work properly and be associated with appropriate documentation.

Main capabilities:

• Stable and maintainable collection of algorithms available for testing
• Machine learning capacities are focused mostly on classification problems
• The platform has standard repo architecture and is available via pip install
• New ML models can be developed faster using building the blocks provided by SimpleAI

Chief limitations:

• The scope of the included algorithms is limited
• There are reported issues with cross-domain interoperability

Price: completely free

User ratings:

• G2: 4.4 (17 reviews)
• Capterra: N/A

Super Learner

This tool may not be able to predict the future, but it provides users with an unmatched level of data-based insights about projected market performance. It’s a collection of algorithms designed for prediction purposes, with the possibility of testing several in parallel before finally choosing the one that best suits the purpose at hand.

Main capabilities:

• Package of prediction-focused algorithms based on the Super Learner model
• Optimal predictor assembling can be completed with a single line of code
• Users can change the hyperparameters of any model or design totally new algorithms
• Multicore and multimode parallelization is enabled

Chief limitations:

• Weak internet connection can seriously disrupt the performance
• Many features are still in development

Price: free

• G2: 4.5 (13 reviews)
• Capterra: N/A

RapidMiner

Decision makers who prefer relying on quantitative data for important tasks can benefit in a big way from their organizations adopting RapidMiner software. It supports all stages of the data collection and management process and allows businesses to use and train pre-built AI algorithms to expand and accelerate this aspect of their operations.

Main capabilities:

• Comprehensive AI-based solution for predictive analytics and business intelligence
• Visual workflows provide instant picture of the model structure
• Data science projects can be completely automated
• AutoModel feature empowers easy creation of custom machine learning algorithms

Chief limitations:

• Existing learning materials are focused on the theoretical side and rarely updated
• Python integrations can be difficult to debug

Price: Custom price determined for each implementation based on scope and complexity

User ratings:

• G2: 4.6 (492 reviews)
• Capterra: 4.4 (22 reviews)

Crossing Minds

The primary objective of this AI-driven platform is to boost online engagement and content or product discovery. It uses some of the most advanced deep learning paradigms to create more personalized and focused recommendations. Crossing Minds is doubtlessly one of the machine learning software applications with the broadest spectrums of potential use cases.

Main capabilities:

• Sophisticated tool that uses machine learning to make behavior-based recommendations
• Extensive customization options and easy integration with the existing IT system
• Proven capacity to increase conversion rates for enterprises in many industries
• Excellent customer support before and after implementation

Chief limitations:

• Not well suited for small or medium companies
• Some dashboards are not well organized

Price: 30 day trial period is offered, full solution is priced at demand

User ratings:

• G2: 4.9 (16 reviews)
• Capterra: no reviews yet

V7

Deep learning models are only as good as the data they are fed, and V7 helps organizations easily obtain large, high-quality datasets consisting of annotated visual material. Since its inception in 2018, this platform has quickly grown into a keystone of machine learning research and its relevance will only increase going forward as the demand for labeled data continues to grow.

Main capabilities:

• Useful AI engine for data annotation of images and videos
• The software relies on a class-agnostic neural network to achieve pixel-level precision
• Custom machine learning datasets can be labelled manually or automatically
• Visual data can be uploaded in many different formats, and is cleaned and sorted in the cloud

Chief limitations:

• The platform is too narrowly specialized for visual data annotation
• It occasionally lags when the size of the dataset is too large

Price: free for education use, business implementations are priced on case-by-case basis

User ratings:

• G2: 4.8 (46 reviews)
• Capterra: 4.3 (9 reviews)

SuperAnnotate

Creating training datasets for machine learning algorithms is greatly simplified by the emergence of this software platform. It is intended for professional annotators working with various types of data, and it comes equipped with many practical tools that increase the reliability and efficiency of labeling. The company can also provide skilled annotators for machine learning projects.

Main capabilities:

• Innovative platform specialized for data annotation for ML model training
• Fully integrated AI infrastructure that facilitates faster dataset creation
• Separate editors for images, audio, text, and video
• Built-in project management tools for tracking the progress in real-time

Chief limitations:

• Tools for Natural Language Processing datasets are underdeveloped
• Tracking the performance of individual annotators is not possible

Price: The startup plan is free, Pro plan quote is given at the client’s request

User ratings:

• G2: 4.9 (84 reviews)
• Capterra: no reviews yet

Keras

This library of neural network algorithms is a real treasure for researchers eager to develop their own models. Keras is an open-source platform that tries to keep things simple for the end user while providing full access to the power of AI at any scale. It represents a densely connected ecosystem of valuable assets that inspires and empowers original research.

Main capabilities:

• Deep learning library written in the Python language that is compatible with TensorFlow
• Supports advanced machine learning methodologies such as convolutional neural networks
• Visualization framework and deep learning framework simplify testing of new ideas
• Highly scalable solution that can be deployed practically anywhere

Chief limitations:

• External software is needed to customize the algorithms
• Errors can be difficult to debug due to inconsistent or confusing logs

Price: free (open-source)

User ratings:

• G2: 4.6 (63 reviews)
• Capterra: 4.7 (37 reviews)

Frequently Asked Questions about Machine Learning Software

What is the difference between machine learning and artificial intelligence?

Artificial intelligence (AI) is a broad concept that relates to any technology that exhibits signs of ‘smart’ behavior. Machine learning (ML) encompasses a group of methods for AI model creation based on the statistical analysis of training material. In recent years, traditional machine learning approaches were complemented with so called ‘deep learning’ algorithms that have more powerful analytic capacities.

In which spheres can machine learning software frameworks bring significant advantages?

Some of the most obvious use cases for machine learning software include software development, business intelligence, predictive analytics, data science, etc. There are many different types of machine learning software ranging from algorithm libraries to market-focused tools, so the exact scope of each platform varies.

Is it possible for non-programmers to use machine learning algorithms?

All machine learning tools require a minimum of data literacy, but coding knowledge is optional. Some platforms aimed at researchers who build new models naturally require a much higher level of technological expertise. On the other hand, customer-centric products with narrowly defined roles are typically more user-friendly.

Final Words

Machine learning is a huge field that continues to grow at a rapid pace. Excellent software platforms are emerging that allow faster and better access to AI-powered technologies, covering all aspects of the model building, training, and deployment cycle. While many of these tools are highly specialized and require long preparation to use adequately, the increased availability of machine learning software is slowly bringing the benefits of this amazing technology into the real world. As deep learning methodologies become more refined and new practical implementations are designed, the core software tools enabling these developments could become far more visible to the general population than today.