10 Best Machine Learning Software (2022)

machine learning (ML) is key to the success of many companies in this data-driven world. It is the technique used to enable machines to perform tasks without explicit instructions from humans. Machine learning technologies can ‘learn’ all by themselves by analyzing the data and identifying patterns.

Machine learning algorithms can be pre-designed 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 special tools used for AI and machine learning purposes. Some of this software specializes for things like accounting or recruiting, while other machine learning tools can be tailored to unique data sets.

Here are the 10 best machine learning software:

1. Google Cloud AI Platform

Topping our list of the 10 best machine learning software is Google Cloud AI Platform, which allows you to 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 ​​platform, AutoML and MLOps and provides point-and-click data science with AutoM, as well as advanced model optimization.

Google really brought all of its resources under one roof with AI Platform, which includes a wide variety of ML services such as data preparation, training, tuning, deployment, collaboration, and machine learning model sharing. In the AI ​​Hub, 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 execution environments. It includes many other features such as: Deep learning VMs, Kubeflow pipelines and more.

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

  • AI explanation
  • User-friendly interface
  • Good connection with TPU and TensorFlow
  • Various ML services

2. Azure Machine Learning

Another top 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 enables experts to build ML models with simple scripting and human-understandable coding practices.

One of the key features of the ML software is that it provides MLOps to help organizations build, test, and deploy ML models quickly. 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 key features of Azure Machine Learning:

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

3. IBM Machine Learning

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

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

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

  • Drag and drop data preparation, blending and modeling
  • Text analysis for unstructured data
  • User-friendly 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 via AWS. It includes continuously trained and fully managed 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 helps you through every stage of the ML adoption processes, providing a comprehensive set of AI and ML services, infrastructure, and deployment resources.

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

  • Solve real business problems in any industry
  • Tailor ML to Business Needs
  • Industry Specific Solutions
  • Handles large data sets with multiple servers

5. neural designer

Another top choice for machine learning software is Neural Designer, a powerful ML platform that uses a suite of drag-and-drop, point-and-click tools. The software is especially useful for those who want to deploy neural network models in the engineering, banking, insurance, healthcare, retail and consumer sectors.

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

Here are some of the key features of Neural Designer:

  • Addresses problems with parameter optimization
  • Good memory management with large data sets
  • CPU and GPU optimized calculations
  • User-friendly interface

6. Anaconda

Anaconda is great machine learning software for small businesses or freelancers, used by big names like AT&T and Toyota. It includes a variety of features such as Conda package manager, mirrored or cloud-based repository, environment manager, and unlimited commercial packages and bandwidth.

The machine learning software simplifies package management and deployment and consists of a large number of tools that allow you to easily collect data from sources using machine learning and AI. These features are 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
  • Over 1500 Python/R data science packages
  • Simplifies package management and deployment
  • Tools to easily collect data from sources with ML and AI

7. H2O.ai

H2O.ai, an easy-to-use platform, was named a visionary by Gartner in the 2020 Magic Quadrant for Data Science and Machine Learning Platforms. The AI ​​platform offers fraud prevention, price optimization, anomaly detection and much more.

H2O.ai is open source and can 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 seamlessly deployed on-premises and in the cloud.

Another good selling point for H2O.ai is that it offers scalability in ML algorithms and compatibility with every major programming language, such as Python and Java.

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

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

8. TensorFlow

TensorFlow is a highly popular, open source platform that offers a wide range of capabilities to facilitate machine and deep learning computations. The graphical approach is useful for visualizing data, and it allows easy debugging of nodes using TensorBoard.

TensorFlow is also compatible with Keras, allowing users to code high-level functionalities into it. In addition, the platform is highly scalable, allowing virtually any operation to be performed. Users can develop any kind of system with TensorFlow thanks to its deployability on any machine and the graphical representation of a model.

This has led to TensorFlow being used to develop systems such as Airbnb and Dropbox.

Here are some of the key features of TensorFlow:

  • User-friendly training and debugging process
  • Free to use
  • Huge library of functions for tasks around text, images, video and more
  • Support for multiple GPUs

9. Spell

Near the end of our list is Spell, a machine learning software that’s especially useful for collaboration. The platform specializes in building and managing ML projects in changing environments. It allows users to easily distribute their code to run projects in parallel, access collaborative Jupyter workspaces, and deploy models across Kubernetes-based infrastructures.

Spell also offers easy setup and onboarding features to enable growing teams, as well as intuitive web console tools and command line tools.

Here are some of the key features of Spell:

  • Simple Commands
  • Command-line interface and usability
  • Onboarding Features
  • Useful for high-impact, evolving environments

10. Microsoft Cognitive Toolkit (CNTK)

Closing out our list of the 10 best machine learning software is Microsoft Cognitive Toolkit (CNTK), Microsoft’s AI solution that trains the machine with its deep learning algorithms. It can handle data from Python, C++ and much more.

CNTK is an open-source, commercial-grade distributed deep learning toolkit that allows users to easily create popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurring neural networks (RNNs/LSTms).

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

Here are some of the key features of CNTK:

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

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