10 Best Machine Learning Software

Machine learning (ML) is the key to many businesses’ success in this data-driven world. It is the technique used to enable machines to carry out tasks without receiving explicit instructions by 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 dedicated tools used for AI and machine learning purposes. Some of this software is specialized for things like accounting or recruitment, while other machine learning tools can be adapted to unique data sets.

Here are the 10 best machine learning software:

1. Google Cloud AI Platform

Topping our list of 10 best machine learning software is Google Cloud AI Platform, which enables 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 while offering point-and-click data science with AutoM, as well as advanced model optimization.

Google really brought all of its assets under one roof with AI Platform, covering a wide range of ML services like data preparation, training, tuning, deploying, collaborating, and sharing machine learning models. Its AI Hub is where you can discover, share, and deploy ML models. It’s a catalog of reusable models that can be deployed to one of the execution environments of AI Platform. It includes many other features like Deep Learning VMs, Kubeflow pipelines, and more.

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 top option for ML software is Azure Machine Learning, which includes a lot of great features that enable data scientists to pull data from a wide range of sources. It also allows experts to build ML models with simple scripting and human understandable coding practices.

One of the top features of the ML software is that it delivers 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 schedule, manage, and automate ML pipelines and perform data-drift analysis to improve the model’s 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 background
• Simplifies remote working

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. Through the machine learning software, you can construct 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 carry out a wide range of tasks, such as scaling compute, people, and apps dynamically across any cloud. You can also manage and unify large data sets and models with transparency and visibility.

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

• Drag-and-drop data prep, blending, 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 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 assists you at every stage 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 the engineering, banking, insurance, healthcare, retail, and consumer industries.

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

Here are some of the main features of Neural Designer:

• Handles parameter optimization problems
• Good memory management with big data sets
• CPU and GPU optimized calculations
• Easy-to-use interface

6. Anaconda

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

The machine learning software simplifies package management and deployment, and it consists of a large set of tools that help you 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 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 pretection, price optimization, anomaly detection, and much more.

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

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

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

• Big data support
• Flexible modeling
• Open-source transparency
• Speeds up conversion of data to predictions

8. TensorFlow

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

TensorFlow is also compatible with Keras, enabling its users to code high-level functionality sections in it. Besides this, the platform is highly scalable, with almost every operation being able to be performed. Users can develop any kind of system with TensorFlow thanks to its characteristic of being deployed on every machine and graphical representation of a model.

This has led to TensorFlow being used 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 around text, images, video, and more
• Multi-GPU support

9. Spell

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

Spell also offers simple set-up and onboarding features to enable expanding teams, 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
• Onboarding features
• Useful for high-impact, evolving environments

10. Microsoft Cognitive Toolkit (CNTK)

Closing out our list of 10 best machine learning software is Microsoft Cognitive Toolkit (CNTK), which is 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 toolkit for commercial-grade distributed deep learning, and it allows users to easily combine popular model types like feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTms).

The machine learning tool can either 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 main features of CNTK:

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