10 Best Machine Learning Software (2022)

Machine learning (ML) is key to the success of many businesses in this data-driven world. It is the technique used to allow machines to perform tasks without receiving explicit instructions from humans. Machine learning technologies can “learn” on their own by analyzing data and identifying patterns.

Machine learning algorithms can be pre-engineered to specialize in a specific task, or they can allow the user to implement their own models specific to their data. The rise of data and machine learning has also led to an increase in machine learning software, which are dedicated tools used for AI and machine learning purposes. Some of this software is specialized for things like accounting or recruiting, while other machine learning tools can be tailored to unique data sets.

Here are the top 10 machine learning software:

1. Google Cloud AI Platform

Topping our list of the top 10 machine learning software is Google Cloud AI Platform, which lets you train your machine learning models at scale, host your trained model in the cloud, and use your model to make predictions about new data. It combines the AI, AutoML and MLOps platform while offering point-and-click data science with AutoM, as well as advanced model optimization.

Google has truly brought all of its assets under one roof with AI Platform, covering a wide range of ML services such as data preparation, training, tuning, deployment, collaboration, and sharing of machine learning models. Its AI Hub is where you can discover, share, and deploy ML models. It is a catalog of reusable models that can be deployed in any of the AI ​​Platform runtimes. It includes many other features such as Deep Learning virtual machines, Kubeflow pipelines, etc.

Here are some of the main features of Google Cloud AI Platform:

  • AI explanations
  • Easy to use interface
  • Good connection with TPU and TensorFlow
  • Various ML services

2. Azure Machine Learning

Another great option for ML software is Azure Machine Learning, which includes many great features that allow data scientists to extract data from a wide variety of sources. It also allows experts to create ML models with simple scripts and human-understandable coding practices.

One of the main features of ML software is that it provides MLOps to help organizations quickly build, test, and deploy ML models. The ML lifecycle can be streamlined and users can leverage Azure DevOps or GitHub actions to plan, manage, and automate ML pipelines and perform data drift analysis to improve model performance.

Here are some of the main features of Azure Machine Learning:

  • Pre-trained models
  • Free product add-ons
  • Available for those with limited coding experience
  • Simplifies remote work

3. IBM Machine Learning

The IBM Machine Learning suite is the combination of various products, such as IBM Watson Studio, IBM Watson Machine Learning, IBM Cloud Pak for Data, and IBM Watson Openscale. Using machine learning software, you can build AI models with open source tools, monitor the models, and deploy them with your applications.

IBM Watson Machine Learning Accelerator is a deep learning feature of IBM Watson Studio on IBM Cloud Pak for Data. It helps businesses perform a wide range of tasks, such as scaling compute, people, and applications dynamically on any cloud. You can also manage and unify large datasets and models with transparency and visibility.

Here are some of the main features of the IBM Machine Learning suite:

  • Drag-and-drop data preparation, merging and modeling
  • Text analysis for unstructured data
  • Easy to use API
  • Unlimited modeling

4. Amazon Machine Learning

Amazon Machine Learning has built a strong reputation with users relying on the platform to build, deploy, and run machine learning applications in the cloud through AWS. It includes fully managed, continuously trained natural language processing (NLP), image recognition, and more.

Amazon Machine Learning enables accurate predictions, deep insights from your data, reduced operational overhead, and improved customer experience. The platform supports you through every step of the ML adoption processes, offering a comprehensive set of AI and ML services, infrastructure, and implementation resources.

Here are some of the main features of Amazon Machine Learning:

  • Solve real-world business problems in any industry
  • Tailor ML to business needs
  • Industry specific solutions
  • Handles large datasets using multiple servers

5. Neural Designer

Another top choice for machine learning software is Neural Designer, a high-performance ML platform that uses a series of drag-and-drop, point-and-click tools. The software is especially useful for those looking to deploy neural network models in engineering, banking, insurance, healthcare, retail, and consumer industries.

The easy-to-use platform relies on a well-defined protocol to create neural network models, allowing you to develop AI-powered applications without programming or building block diagrams. It also contains state-of-the-art algorithms for data preparation, model training, test analysis, feature selection, response optimization, and model deployment.

Here are some of the main features of Neural Designer:

  • Handles parameter optimization issues
  • Good memory management with large data sets
  • CPU and GPU optimized calculations
  • Easy to use interface

6. Anaconda

Anaconda is great machine learning software for small businesses or freelancers, and it’s used by big names like AT&T and Toyota. It includes a variety of features such as a Conda package manager, mirrored or cloud-based repository, environment manager, and unlimited commercial packages and bandwidth.

Machine learning software simplifies package management and deployment, and it consists of an extensive set of tools that help you easily collect data from sources using machine learning and the AI. These characteristics explain why Anaconda is a top choice for data scientists and IT professionals.

Here are some of the main features of Anaconda:

  • Free and open-source
  • 1500+ Python/R data science packages
  • Simplifies package management and deployment
  • Tools to easily collect data from sources using ML and AI

7. H2O.ai

A user-friendly platform, H2O.ai was named a Visionary by Gartner in the 2020 Magic Quadrant for Data Science and Machine Learning Platforms. The AI ​​platform offers fraud protection, price optimization, anomaly detection and more.

H2O.ai is open source, and it can be used to benefit businesses in many ways. The platform accelerates the conversion of data into predictions, leverages data lakes and silos, and enables AI workloads to be deployed seamlessly on-premises and in the cloud.

Another selling point of H2O.ai is that it provides scale in ML algorithms and compatibility with all major programming languages, such as Python and Java.

Here are some of the main features of H2O.ai:

  • Big data support
  • Flexible modeling
  • Open source transparency
  • Accelerates the conversion of data into predictions

8. TensorFlow

TensorFlow is a very popular open-source platform that offers a wide range of features to facilitate machine and deep learning computations. Its graphical approach is useful for visualizing data and allows easy debugging of nodes with the use of TensorBoard.

TensorFlow is also compatible with Keras, allowing its users to code sections of high-level functionality into it. Moreover, the platform is highly scalable, almost any operation can be performed. Users can develop any type of system with TensorFlow thanks to its feature of being deployed on each machine and the graphical representation of a model.

This led to using TensorFlow to develop systems like Airbnb and Dropbox.

Here are some of the main features of TensorFlow:

  • User-friendly training and debugging process
  • Free to use
  • Massive library of functions for tasks related to text, images, video, etc.
  • Multi-GPU support

9. Fate

Towards the end of our list is Spell, which is a particularly useful machine learning software for collaboration. The platform specializes in creating and managing ML projects in evolving environments. It allows users to easily distribute their code to run projects in parallel, access collaborative Jupyter workspaces, and deploy models to Kubernetes-based frameworks.

Spell also offers easy setup and integration features to help teams grow, as well as intuitive web console tools and command-line tools.

Here are some of the main features of Spell:

  • Simple commands
  • Command line interface and usability
  • Integration Features
  • Useful for high impact and changing environments

10. Microsoft Cognitive Toolkit (CNTK)

To close our list of top 10 machine learning software, Microsoft Cognitive Toolkit (CNTK) is Microsoft’s artificial intelligence solution that trains the machine with its deep learning algorithms. It can handle data from Python, C++ and more.

CNTK is an open-source toolkit for commercial-grade distributed deep learning, and it allows users to easily combine popular model types such as feedforward DNNs, convolutional neural networks (CNNs), and neural networks. recurrent neurons (RNN/LSTms).

The machine learning tool can either be included as a library in your Python, C# or C++ programs or used as standalone machine learning software through its own model description language.

Here are some of the main features of CNTK:

  • open-source
  • Commercial-grade distributed deep learning
  • Easily combine popular model types
  • Included as a library or standalone tool

Sherry J. Basler