Machine Learning Tools

Machine Learning Tools Overview

Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

They can be used, for example, to build recommendation engines, predict search patterns, filter spam, build news feeds, detect fraud and security threats, and much more. There are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced.

Supervised algorithms are machine learning tools with training wheels. They require a person to program both the input and the desired output, as well as provide feedback as to the accuracy of the end results.

Unsupervised algorithms require very little human intervention by instead using an approach called “deep learning” to review massive banks of data and arrive at conclusions based on previous examples of training data; they are, therefore, generally used for more complex processing tasks such as image recognition, speech-to-text, and natural language generation.

Semi-supervised algorithms tend to fall in the middle ground.

Reinforced algorithms force models to repeat a process until it produces the most favorable outcomes. Attempts that produce these favorable outcomes are rewarded and attempts that produce unfavorable results are penalized until the algorithm learns the optimal process.

Top Rated Machine Learning Products

TrustRadius Top Rated for 2022

These products won a Top Rated award for having excellent customer satisfaction ratings. The list is based purely on reviews; there is no paid placement, and analyst opinions do not influence the rankings. Read more about the Top Rated criteria.

Machine Learning Tools TrustMap

TrustMaps are two-dimensional charts that compare products based on trScore and research frequency by prospective buyers. Products must have 10 or more ratings to appear on this TrustMap.

Machine Learning Products

(1-25 of 154) Sorted by Most Reviews

The list of products below is based purely on reviews (sorted from most to least). There is no paid placement and analyst opinions do not influence their rankings. Here is our Promise to Buyers to ensure information on our site is reliable, useful, and worthy of your trust.

RStudio
Customer Verified
Top Rated

RStudio is a modular data science platform, combining open source and commercial products. The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to…

Key Features

  • Visualization (22)
    89%
    8.9
  • Connect to Multiple Data Sources (21)
    84%
    8.4
  • Extend Existing Data Sources (22)
    83%
    8.3
IBM Watson Studio on Cloud Pak for Data

IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies…

Key Features

  • Visualization (21)
    77%
    7.7
  • Connect to Multiple Data Sources (21)
    76%
    7.6
  • Extend Existing Data Sources (21)
    74%
    7.4
DataRobot
Customer Verified
Top Rated

The DataRobot AI Cloud platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users…

Key Features

  • Automated Machine Learning (33)
    93%
    9.3
  • Automatic Data Format Detection (33)
    81%
    8.1
  • Visualization (33)
    79%
    7.9
Jupyter Notebook

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and…

Key Features

  • Visualization (23)
    93%
    9.3
  • Interactive Data Analysis (23)
    91%
    9.1
  • Connect to Multiple Data Sources (23)
    85%
    8.5
Kira Systems

Kira, now from Litera (acquired August, 2021) is software that searches and analyzes contract text. Kira offers pre-built, machine learning models covering due diligence, general commercial, corporate organization, real estate and compliance. Using Kira Quick Study, anyone can train…

Databricks Lakehouse Platform (Unified  Analytics Platform)

Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data…

TensorFlow

TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.

OpenText Magellan

OpenText Magellan Analytics Suite leverages a comprehensive set of data analytics software to identify patterns, relationships and trends through data visualizations and interactive dashboards.

Oracle Machine Learning

Oracle Machine Learning (formerly Oracle Advanced Analytics) combines the Oracle database with Oracle Data Miner and SQL as well as R programming language functionality, providing a complete predictive analytics suite.

Keras

Keras is a Python deep learning library

Amazon SageMaker

Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Forecast

Amazon Forecast is a fully managed service that uses machine learning to deliver accurate forecasts. Amazon Forecast can use historical time series data (e.g., price, promotions, economic performance metrics) to create accurate forecasts for businesses.

Plotly Dash

Plotly headquartered in Montreal creates data visualization and UI tools for ML, data science, engineering, and the sciences with language support for Python, R, Julia, and JS. Plotly's Dash aims to empower teams to build data science and ML apps that put Python, R, and Julia in…

Key Features

  • Interactive Data Analysis (5)
    90%
    9.0
  • Visualization (5)
    88%
    8.8
Google Cloud AI

Google Cloud AI provides modern machine learning services, with pre-trained models and a service to generate tailored models.

IBM Watson Discovery

IBM offers Watson Discovery, a natural language processing (NLP) application with options to measure sentiment, detect entities, semantic roles, and other concepts.

Course5 Discovery

Course5 Discovery is an AI-powered Augmented Analytics solution that aims to deliver fast, relevant, actionable and humanized insights across multiple consumption mediums and personas to create an insights-first culture that rewards data-driven decision making. Course5 Discovery…

Azure Machine Learning

Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.

botkeeper

Boston based Botkeeper is the world's first and original robotic bookkeeper. The Botkeeper solution uses a combination of skilled accountants, machine learning, and AI to provide the best bookkeeping at the lowest possible cost. Instead of replacing your existing accounting software,…

InRule

InRule Technology in Chicago, Illinois offers business rules management software.

Iguazio

Iguazio, headquartered in Herzliya, provides a Data Science Platform to automate machine learning pipelines. It aims to accelerate the development, deployment and management of AI applications at scale, enabling data scientists to focus on delivering better, more accurate and more…

IBM Watson Visual Recognition

IBM offers Watson Visual Recognition, a machine learning application designed to tag and classify image data, and deployable for a wide variety of purposes.

IBM Watson Machine Learning

IBM Watson Machine Learning allows businesses to deploy self-learning models at scale, allowing AI to deployed in applications and available free to try, free for limited use (5 deployed models and 5,000 predictions per month), or at cost for high workloads priced per thousands of…

Cloudera Data Platform

Cloudera Data Platform (CDP), launched September 2019, is designed to combine the best of Hortonworks and Cloudera technologies to deliver an enterprise data cloud. CDP includes the Cloudera Data Warehouse and machine learning services as well as a Data Hub service for building custom…

Tessian Defender

Tessian Defender automatically prevents business email compromise, spear phishing, account takeover (ATO), and other targeted email attacks. Powered by Tessian’s proprietary Human Layer Security Engine, Defender analyses millions of data points for every inbound email and detects…

Learn More About Machine Learning Tools

What are Machine Learning Tools?

Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

They can be used, for example, to build recommendation engines, predict search patterns, filter spam, build news feeds, detect fraud and security threats, and much more. There are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced.

Supervised algorithms are machine learning tools with training wheels. They require a person to program both the input and the desired output, as well as provide feedback as to the accuracy of the end results.

Unsupervised algorithms require very little human intervention by instead using an approach called “deep learning” to review massive banks of data and arrive at conclusions based on previous examples of training data; they are, therefore, generally used for more complex processing tasks such as image recognition, speech-to-text, and natural language generation.

Semi-supervised algorithms tend to fall in the middle ground.

Reinforced algorithms force models to repeat a process until it produces the most favorable outcomes. Attempts that produce these favorable outcomes are rewarded and attempts that produce unfavorable results are penalized until the algorithm learns the optimal process.

Machine Learning Tools' Features & Capabilities

Most vendors offering machine learning tools include:

  • Data collection and preparation
  • Model building
  • Training and application deployment

Additional Tools

Some vendors also offer additional tools to

  • Store aggregated data on the Cloud
  • Build models with easy drag and drop capabilities
  • Access libraries with pre-modeled routines and functions

Machine Learning Tools Comparison

When beginning your process for a machine learning tool with which to get started, walk through the following flow of questions to help you narrow down your options:

  • Am I looking for a managed machine learning platform? (Think time-and-cost efficiency.)
  • Do I need a mobile-supported tool?
  • For what scripting language do I need support? (R, Python, Java, C++, etc.)
  • If you're on a budget, ask yourself: Can I effectively manage my training data with the storage space I have? (You may need to consider moving to the cloud, which is typically more expensive.)
  • Do I need support for multiple model types? (Binary, regression, multi-class, etc.)
  • Would I prefer having an extensive library with which to start?
  • Are my projects expected to grow (and thus requiring a need for scalability)?
  • Do I need a full cycle deep learning system?

Ready to start comparing your shortlist head-to-head? Start a Machine Learning Tools comparison

Pricing Information

Many vendors offering machine learning tools will offer a free trial or a free version with a limited batch of predictions. There are several free and open-source machine learning platforms, like Google's Tensorflow, for developers on a budget. For paid plans, prices are generally Pay-As-You-Go, tiered by price per hour and training units, typically starting at $0.10/Unit/Hour. Deep learning capabilities are typically more expensive, at $1+/GPU/Hour.

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Frequently Asked Questions

What do machine learning tools do?

Machine learning tools absorb and interpret data, which helps them create models that can be used for future process automation. Their highly advanced algorithms make predictions and better decisions based on this data. Over time, these tools learn and adapt, creating intelligent learning capabilities for applications. The four types of machine learning tools include supervised, unsupervised, semi-supervised, and reinforced.

What are the benefits of using machine learning tools?

Machine learning tools identify important patterns in large quantities of data. They consistently improve the speed and accuracy of their predictions. These tools also provide automation, which saves time and makes processes more efficient.

How much do machine learning tools cost?

There are several free and open-source machine learning platforms, such as Google's Tensorflow, for developers on a budget. Paid plans are generally tiered by price per hour and training units, typically starting at $0.10/unit/hour. Advanced learning capabilities are typically more expensive, starting at $1+/GPU/hour.