Machine Learning Tools

TrustRadius Top Rated for 2024

Top Rated Products

(1-3 of 3)

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Saturn Cloud

Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment.…

2
DataRobot

The DataRobot AI 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 AI to…

3
KNIME Analytics Platform

KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.

All Products

(1-25 of 286)

1
Qlik Sense

Qlik Sense® is a self-service BI platform for data discovery and visualization. It supports a full range of analytics use cases—data governance, pixel-perfect reporting, and collaboration. Its Associative Engine indexes and connects relationships between data points for creating…

2
Posit

Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.

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…

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4
DataRobot

The DataRobot AI 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 AI to…

5
Anaconda

Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage…

6
KNIME Analytics Platform

KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.

7
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…

8
Databricks Data Intelligence 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…

9
Saturn Cloud

Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment.…

10
TensorFlow

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

11
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.

12
SAS Viya

An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.

13
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.

14
Vertex AI

Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.

15
IBM SPSS Modeler

IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management…

16
Google Cloud AI

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

17
Hugging Face

Hugging Face is an open-source provider of natural language processing (NLP) technologies.

18
Keras

Keras is a Python deep learning library

19
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.

20
IBM watsonx.ai

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation…

21
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.

22
Pytorch

Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.

23
Dataiku

Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.

24
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.

25
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…

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.

What are the best machine learning tools products?

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.