illustration of a brain with wire and nodes for machine learning software

10 Best Machine Learning Software [2021 List]

Machine learning (ML) is just one way that companies can transform how they use big data to best understand their customers’ behavior, satisfaction, and/or loyalty. ML can hunt for patterns and anomalies that users didn’t even think to query about for themselves. 

Some machine learning algorithms are pre-designed to specialize in one thing, but in this article, we are looking specifically at machine learning applications that enable the user to implement their own machine learning models for whatever data you have.

I will present some of the best machine learning tools and outline how users can best leverage each for deep learning, data mining, and dataset visualization.

What Is Machine Learning Software?

Machine learning software is any dedicated tool used for artificial intelligence, self-iteration based on data, unsupervised learning, and other ML classifiers. Much software at work today has elements of machine learning, like in email filtering and computer vision. 

There are also machine learning specialty software for things like simulation, recruitment, architecture, and accounting. Some ML toolkits, like the ones listed in this article, can be custom-built to adapt to your unique data sets and workflow needs.

The Best Machine Learning Software List

Here’s a shortlist of the best machine learning software:

  1. IBM Machine Learning
  2. Google Cloud AI Platform
  3. Azure Machine Learning
  4. Amazon Machine Learning
  5. Neural Designer
  6. H2O.ai
  7. Anaconda
  8. TensorFlow
  9. cnvrg.io
  10. Spell

Machine Learning Software Comparison Criteria

Here’s a summary of my evaluation criteria: 

  1. User Interface (UI): Does the software provide clean and attractive data processing and data analytics tools? Are large-scale computations presented in a way that enables easy drill-downs?
  2. Usability: Does the software work seamlessly with different programming languages and every popular machine learning library? Do advanced features offer tutorial help either on the company’s website or as in-app wizards?
  3. Integrations: Does the ML software connect easily with other popular programming tools and ML libraries like Java, Apache Mahout and Apache Spark, Hadoop, Keras, Pytorch, and Scikit-learn?
  4. Value for $: How appropriate is the price for the features, capabilities, and use case? Do they offer easy, flexible scaling by Capacity Unit-Hour (CUH)?

Machine Learning Software Key Features

  1. Pattern recognition, classification, and regression
  2. Predictive analytics
  3. Dimensionality reduction functionality 
  4. Support vector machines
  5. Connection with popular machine learning libraries, such as Apache Spark MLlib 
  6. Works with key programming languages (Scala, Java, C++, R, Python, etc.)
  7. End-to-end open-source machine learning

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Overviews Of The 10 Best Machine Learning Software

Here’s a brief description of each machine learning platform on my list showing what it does best, plus screenshots to showcase some of the features.

1. IBM Machine Learning – Best free, open-source machine learning software 

IBM Machine Learning Screenshot
Download your projects in a variety of model formats from TensorFlow to PyTorch.

The IBM Machine Learning suite lets you mix and match between a few different products, like IBM Watson Studio, IBM Watson Machine Learning, IBM Watson OpenScale, and IBM Cloud Pak for Data. Users are able to build AI models using open source tools, deploy AI models with your apps, and monitor AI models. IBM Machine Learning also has a robust free lite plan for 20 CUH and a maximum of 2 parallel decision optimization batch jobs per deployment.

IBM Machine Learning costs from $0.50/CUH and offers a free lite plan with 20 capacity unit-hours. Paid plans also offer a free 30-day trial.

Pros:

  • Drag-and-drop data prep, blending, and modeling
  • Text analytics for unstructured data
  • Unlimited modeling without overages
  • Well documented, easy-to-use API

Cons:

  • Must launch each service in separate tabs (no single overview)
  • Implementation journey is a time commitment
  • Some restrictions when adjusting results and parameters while training models

2. Google Cloud AI Platform – Best low- and no-code machine learning framework

Google Cloud AI Platform Screenshot
Build pipelines and run experiments with Google’s customary user-friendly UX.

Google Cloud AI platform brings together its AI Platform, AutoML, and MLOps for a comprehensive and unified experience. Their platform scales to the user’s skill level, offering both point-and-click data science using AutoML as well as advanced model optimization. They provide both code-based and no-code tools for a simplified machine learning experience.

Google Cloud AI Platform costs from $0.19/hour and offers a free $300 credit for your first 90 days.

Pros:

  • AI explanations and What-If validation testing
  • Very easy to use interface
  • Good connection with TPUs and TensorFlow
  • Good out-of-the-box CV algorithms and video processing modules

Cons:

  • Not well suited for hybrid cloud deployments
  • Additional library support would be welcome
  • Limited to running 25 models in parallel

3. Azure Machine Learning – Best for free add-on products and connectors

Azure Machine Learning Screenshot
Two-class boosted decision tree built with easy point-and-click Microsoft functions.

Microsoft’s Azure Machine Learning lets users quickly and easily build, train, and deploy machine learning models. QA leads will enjoy being able to employ automated machine learning that more quickly identifies suitable algorithms and hyperparameters. Many of their accentuating services are free forever, including certain app services, event processing, and automation for up to 500 minutes of job runtime.

Azure Machine Learning costs from $0.333/hour and offers 12 months free with a $200 credit for the first 30 days.

Pros:

  • Excellent pre-trained models
  • Adaptable for users with limited coding background
  • Robust suite of free product add-ons
  • Lengthy free trial plus dollar-value credits

Cons:

  • Can be challenging to add Python code and run
  • No easy connection to Tableau
  • Could handle heavy statistical models better

4. Amazon Machine Learning – Best for those in the AWS ecosystem

Amazon Machine Learning Screenshot
Leverage Amazon’s deep feedback mechanisms to rate a ML model’s quality.

Amazon Machine Learning empowers users to build, deploy, and run machine learning applications in the cloud through AWS. Your team will be able to tap into continuously trained and fully managed natural language processing (NLP), deep learning-based image recognition, and a flexible allotment of t2.medium notebook usage per month. Single-click processes and preset versioning rules make getting things running quick and easy.

Amazon Machine Learning costs from $0.42/hour and offers a 12-month free trial.

Pros:

  • Easily handles large datasets using multiple servers
  • Powerful auto-scaling model server
  • Visually track development of ML modules
  • Customizations are easy to adjust and modify

Cons:

  • Best for those already in the Amazon ecosystem
  • Advanced programming background required 
  • Cannot schedule training jobs at this time

5. Neural Designer – Best for their high GPU training speed

Neural Designer Screenshot
Dashboard view with datasets, deep architecture, performance measurement, and training strategy.

Neural Designer is a high-performance machine learning platform that lets you skirt writing code and building block diagrams with a series of drag-and-drop, point-and-click tools. They boast an average GPU training speed of 417K+ samples per second, which is faster than many competing platforms. Neural Designer is entirely programmed in C++, which sacrifices some ease of use qualities but offers better performance speeds instead.

Neural Designer costs from $2495/user/year and offers a free analysis of up to 1,000 data rows.

Pros:

  • Excellent presentation of data processing results
  • Handles parameter optimization problems well
  • Good memory management for importing big data sets
  • Fast calculations using CPU and GPU optimization

Cons:

  • No cloud-based tool at this time
  • Inflexible pricing plan
  • Some limitations around automating processes

6. H2O.ai – Best open source integration with Spark

H2O.ai Screenshot
Flashy, modern UI backed by impactful ML experiments and training data.

H2O.ai is a user-friendly, accessible AI platform that was named a Visionary by Gartner in the 2020 Magic Quadrant for Data Science and Machine Learning Platforms. Fraud prevention, anomaly detection, and price optimization are some items they offer. H2O Sparkling Water integrates with Spark for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark.

H2O.ai costs from $0.046/hour and offers a free 21-day trial.

Pros:

  • Big data support with H2O’s Sparkling Water
  • Flexible modeling including Ensemble
  • Flexible horizontal scaling via provisioning dynamic clusters
  • Excellent commitment to open-source transparency

Cons:

  • More cutting-edge algorithms would be welcome
  • Some documentation could be refined
  • Charting and visuals could use a makeover

7. Anaconda – Best machine learning software for small businesses and freelancers

Anaconda Screenshot
Anaconda Navigator lets you scan what environment you have available to you.

Anaconda is a hearty platform for the MLOps life cycle, and is used by the likes of American National Bank, AT&T, Toyota, and Goldman Sachs. Standard features include a Conda package manager, unlimited commercial packages and bandwidth, a mirrored or cloud-based repository, and an environment manager. Their Individual subscription tier is perfect for freelancers: free to use, thousands of open-source packages and libraries, and 7500+ Conda packages.

Anaconda costs from $14.95/mo and also offers a free Individual Edition.

Pros:

  • Robust open-source freeware package
  • Good management of custom environments
  • Friendly with different versions of Python and its libraries
  • Pre-installs the most useful libraries and packages at the get-go

Cons:

  • Integration with Git/versioning software would be an improvement
  • Source-controlled environments and custom repositories limited to Enterprise Edition
  • Excludes PyCharm, Atom, and Docker

8. TensorFlow – Best for ML models on mobile and IoT devices

TensorFlow Screenshot
TensorFlow uses graphs as the format for saved models when it exports them from Python.

TensorFlow boasts easy model building that can be customized to address image reading and classification, buyer and seller experience optimization, mobile proof-of-purchase elements, job completion predictions, and more. Plus, they offer a deep learning framework for on-device inference. Users can pick a new or retrained ML model, convert it to a compressed flat buffer, and easily load the resulting .tflite file into any mobile or embedded device.

TensorFlow is free to use.

Pros:

  • Streamlined, user-friendly training and debugging process
  • Completely free to use
  • Huge library of functions for tasks around text, images, tabular, video, etc.
  • Multi-GPU support with simple, clear documentation

Cons:

  • Some error messages are convoluted/hard to understand
  • Not ideal for build tools with poor Python support
  • Better bindings with the JVM would be welcome

9. Cnvrg.io – Best machine learning software for the gaming industry

Cnrvg.io Screenshot
Get a birds-eye-view of your activity, experiment frequency, and project segmentation.

Cnvrg.io is a scalable full-stack solution for MLOps and model management. They are a leading data science platform with collaborative ML environments, container-based infrastructure, and production-ready infrastructure with native Kubernetes cluster orchestration. Cnvrg.io offers industry-specific assistance for the gaming industry, such as the optimization of monetization, churn reduction, and in-game experience personalization.

Cnvrg.io costs from $9500/instance and offers a free 14-day trial. They also offer a free version of their platform called CORE.

Pros:

  • Works with any framework or programming language
  • Excellent free-to-use options
  • Releases industry-first dataset caching for ML
  • Good for hybrid machine learning

Cons:

  • Over 3000 jobs/year will require their Premium edition
  • Feature-set is still a work in progress, as they are new to the market
  • Better for SMB rather than large enterprises

10. Spell – Best machine learning software for collaborative teamwork

Spell Screenshot
Workspace is an instance of a Jupyter Notebook/JupyterLab running on the cloud.

Spell specializes in building and managing machine learning projects in high-impact, changing environments. Users can easily distribute their code to run projects in parallel, deploy models in Kubernetes-based infrastructure, and access collaborative Jupyter workspaces and resources. They have simple set-up and onboarding features for shifting and growing teams, including intuitive web console tools and simple command line tools.

Spell costs from $329/user/month and also offers a freemium version with $10 of GPU credit.

Pros:

  • Intuitive tools and simple commands
  • Excellent command-line interface and usability
  • Takes care of infrastructure for quick-start projects

Cons:

  • Free version runs only on their shared community cluster
  • Models need to run with Python

Other Machine Learning Software 

Here’s a few more that didn’t make the top list.

  1. Torch – Best for linear algebra routines
  2. Weka – Best for image and text classification
  3. TADA Artificial Intelligence – Best for small data
  4. Kount – Best for bots and account takeover fraud

Which Machine Learning Software Have You Used?

What machine learning software do your data scientists use? What tools do you use to optimize your training data and craft ideal neural networks? What are your best tips for real-time data analysis and data preparation? What ML models are your go-to? Sound off in the comments!

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