Last Updated: January 05, 2023
machine learning platforms
Published On: January 05, 2023

Companies are widely deploying artificial intelligence worldwide to enhance customer experience and satisfaction and make predictive analyses by processing huge volumes of big data to derive actionable business intelligence. Machine Learning based apps are in high demand, and the trend continues to grow.

What is Machine Learning?

In order to simulate the way humans learn, the field of Artificial Intelligence known as Machine Learning employs large amounts of data and complex algorithms. The algorithms designed for machine learning can make predictions and classifications by processing big data. Machine Learning is a goldmine for data scientists to pick up key insights in mining projects. Moreover, machine learning help businesses identify key business and customer questions based on data and develop automatic mechanisms, such as chatbots, to answer them.

ML is used for several purposes, such as customer service, image recognition, speech recognition, recommender system, auto-tagging, email filtering, and more.

What are Machine Learning Platforms?

Machine Learning Platforms provide the tools required to develop and deploy machine learning algorithms to automate and accelerate big data processing. The tools on a machine learning platform allow developers to design intelligent algorithms and mathematical models to implement unique solutions for their products and services. Machine learning platforms are used to develop predictive applications capable of processing huge volumes of data.

The machine learning platforms can be either cloud based or on-premise. Most machine learning platforms are cloud based, enabling a high level of scalability and eliminating the need to maintain on-site infrastructure.

There are many options if you are looking for the best platform for machine learning. The top 12 ML platforms for the year 2023 have been listed here.NameFeaturesPyTorch·Building small projects and prototypes

·Large collection of tools and libraries name Analytics Platform·End-to-end data analysis, integration, and reporting
·Create analytical workflows without codingTensorFlow·Customized ML model building
·Large collection of tools to train and deploy models

Name Features
PyTorch · Building small projects and prototypes

· Large collection of tools and libraries

KNIME Analytics Platform · End-to-end data analysis, integration, and reporting

· Create analytical workflows without coding

TensorFlow · Customized ML model building

· Large collection of tools to train and deploy models

Amazon SageMaker · Fully managed workflows, tools, and infrastructure

· Visual interface

Keras · Quick prototyping

· Processes large volumes of data

SAS · High-end APIs

· Automatic generation of data insights

Microsoft Azure Machine Learning Studio · Builds ML models without coding

· Suited for building  predictive models · Support for statistical and machine learning algorithms

· Fraud detection, predictive maintenance, and risk prediction

Neural Designer · Builds AI apps without coding

· Supports advanced ML algorithms

Alteryx Analytics · Automates manual data into workflows

· A large number of data connectors

Gluon · Simple API to build ML models

· Dynamic neural network

Spell · Wide range of tools

· Collaborative workspace and onboarding tools


PyTorch is one of the best open source machine learning platforms. It is based on the torch library and is specifically suited for training, building, and deploying small projects and prototypes. PyTorch has a Python interface and a C++ front end. The torch. Distributed backend enables scalable distributed training and performance optimization in research and production.

Key features

  • Large collection of tools and libraries to support the development of applications for a range of purposes, such as reinforcement learning and computer vision
  • TorchServe tool to enable the deployment of PyTorch models at scale
  • Allows the use of standard debuggers such as PyCharm and PDB
  • Flexibility to make changes to the model architecture even during the training process due to the dynamically updated graph operated by PyTorch
  • Supported on major cloud platforms

KNIME Analytics Platform

KNIME Analytics Platform provides end-to-end data analysis, integration, and reporting. It is an open source platform that allows users to access, blend, visualize and analyze data without coding. KNIME has an intuitive drag-and-drop interface that allows users to create analytical workflows without coding.

There are more than 2000 nodes to create workflows. The intuitive interface enables easy joining of the nodes to create workflows. The nodes allow many actions on the data, such as manipulation, transformations, and mining.

Key Features

  • Integrations to all popular machine learning libraries and over 300 connectors to data sources
  • Multi-core systems for parallel execution
  • Fulfills multiple data science purposes such as predictive modeling, automating spreadsheets, ETL, and more
  • Blends various types of data such as texts, images, integers, strings, sounds, molecules, networks, and more
  • Provides scalability


TensorFlowis among the top machine learning platforms. It is an open source platform developed by Google. TensorFlow is based on JavaScript and has many tools and resources for training and deploying machine learning models. It allows users to build machine learning models for web browsers and mobiles or embedded devices. TensorFlow Extended allows users to build, train and deploy machine learning models in larger production environments.

Key Features

  • Offers customized model building for various purposes such as buyer and seller experience optimization, image reading and classification, job completion predictions, and more
  • Widely used for data integration functions such as combining graphs, SQL tables, and images.
  • Deploying models on both computing clusters and Android and iOS devices
  • Python is the most preferred programming language for working with TensorFlow

Amazon SageMaker

Amazon SageMaker is one of the top cloud based machine learning platforms. Developed by Amazon Web Services, it allows developers to make and deploy machine learning models on embedded systems and edge devices through the cloud services of AWS. Amazon SageMaker comes equipped with fully managed tools, workflows, and infrastructure to train, build and deploy machine learning models for various purposes.

Key Features

  • Enables users to access, label, and process large volumes of both structured and unstructured data for machine-learning applications
  • Deploys a plethora of tools for machine learning models, such as a no-code interface for business analysts and IDEs for data scientists
  • SageMaker Canvas for making predictions using a visual interface
  • Builds training datasets having a high level of accuracy


Keras is an open-source machine and deep-learning platform. It is easy to use and extensible because it is written in Python. The main advantage of Keras is its ability to process large volumes of data quickly and quicken the training time for machine learning models due to its built-in support for data parallelism.

Keras offers simple APIs, reduces the number of actions required to implement a common code, and provides clear explanations of user errors. The time taken for building and deploying prototype machine learning models is less with Keras.

Key Features

  • Supports almost every kind of neural network model
  • Runs seamlessly on both CPU and GPU
  • Helps in writing precise and readable codes
  • Allows quick prototyping of simple concepts


SAS Visual Data Mining and Machine Learning Platform enable users to access data in any format from any source. It automatically generates insights to enable users to identify common and important variables across models and provides assessment results for all the models. The pipeline generated by the platform dynamically adjusts to the data.

Key Features

  • Provides access to high level APIs for many functionalities such as natural language processing, computer vision, forecasting, speech processing, and more
  • Enables users to combine structured and unstructured data into integrated machine learning programs
  • Visual interface for the analytical lifecycle
  • Exports modeling insights as PDF reports

Microsoft Azure Machine Learning Studio

Microsoft Azure Machine Learning Studio is one of the most popular machine learning cloud platforms. It has a user-friendly interface for building, training, and deploying machine learning models. This platform perfectly suits users wanting to build predictive analytics models for big data. Azure also enables users to build, deploy and share advanced analytics through the cloud.

Azure deploys tools to enable data scientists and machine learning engineers to automate daily workflows. At the same time, app developers get access to tools for integrating machine learning models in apps and services.

Key Features

  • Swift and easy deployment of ML models at scale
  • Jupyter notebook servers integrated into the studio allow users to write and run their codes.
  • ML designer enables the building of ML models without writing code by simply dragging and dropping datasets.
  • Data visualization of various metrics of the ML models is an advanced cloud-based AI and machine learning platform. It is a fully open-source platform providing support for statistical and machine learning algorithms to build AI and machine learning models and apps with high accuracy and transparency. H2O is also a deep learning platform widely preferred by data scientists.

Key Features

  • Accurate detection of transactional frauds and abnormal patterns in financial transactions
  • Builds models for detecting anomalies in the supply chain and pick up specific points for investigation by the supply chain manager
  • Risk prediction using AI when giving money on credit to borrowers
  • Predictive maintenance analysis
  • More than 30 pre-trained image transformers and models for image processing

Neural Designer

Neural Designer is a high-end machine learning platform that enables users to build AI applications without coding or building block diagrams by deploying drag-and-drop and point-and-click tools. This platform is a specialist in neural networks, which is the most powerful machine learning technique. Neural Designer is programmed completely in C++ language.

Key Features

  • Advanced machine learning algorithms to recognize patterns, detect relationships, and predict trends from big data
  • A plethora of industry-specific AI solutions
  • Average GPU training speed of over 417k samples per second

Alteryx Analytics

Alteryx Analytics is one of the best data science platforms. This platform enables data scientists to build models for automating manual data into repeatable analytical workflows. It provides full freedom to deploy analytic models and supports almost all kinds of data sources and visualization tools. Simplification of the complexity of analytics by automation leads to better decisions and outcomes.

Key Features

  • Hundreds of data connectors to databases, applications, and public clouds to quickly and easily build data pipelines.
  • Provides the highest standards of data protection, security, privacy, compliance, and reliability
  • Automation of workflows to ensure that systems and decision-makers get timely updates without manual intervention


Gluon is an open source machine and deep learning platform which enables developers to build machine learning models swiftly and easily. It has many plug-and-play neural network blocks, such as optimizers, pre-defined layers, and initializers. Gluon enables users to build neural networks using clear and concise codes.

Gluon combines the training algorithm and neural network model to make the development process more flexible. The neural network is dynamic, which is much easier for the developers to manage and build new model architectures without compromising on speed.

Key Features

  • Clear and simple API simplifies the building of machine learning models
  • Enables users to define and manipulate ML models like other data structures
  • Suitable for both machine learning beginners as well as experts


Spell is a platform for training and deploying machine leading models swiftly and efficiently, particularly in high-impact environments. It comes with tools for various purposes, such as model training, hyperparametersearch, experiment management, and more. The users can run multiple machine learning projects in parallel by distributing codes. The interactive UI provides complete visibility to all the ML models.

Key Features

  • Resources library for models and datasets
  • Automated workflows, metrics, and analytics
  • Collaborative Jupyter workspaces and resources
  • Simple onboarding tools for growing teams
  • Available as on-premise, cloud, and hybrid

How Can You Help In Machine Learning Projects?

Our team has the expertise to know the exact machine learning and AI requirements for various projects. Whether it is a startup or a big enterprise, our developers provide the best machine learning solutions, from selecting the correct platforms and tools to building, training, and deploying ML models.


Machine Learning has a huge potential to deliver the best results for companies, from business operations to customer satisfaction. The top machine learning platforms listed here would help you choose the best one per your specific needs. You can also get in touch with experts to get sound advice regarding the best platform.


Machine Learning platforms provide tools to develop and deploy machine learning models for automating data workflows, accelerating data processing, and building other functionalities.

Yes. There are many such as PyTorch, Gluon,, and more.

Some examples are Amazon SageMaker, Microsoft Azure Machine Learning Studio, and

Multiple options exist, such as PyTorch, TensorFlow, SAS,, Neural Designer, and more.

They have Supervised Learning, Semi-Supervised Learning, Self Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
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Jayanti Katariya

Jayanti Katariya is the CEO of Moon Technolabs, a fast-growing IT solutions provider, with 18+ years of experience in the industry. Passionate about developing creative apps from a young age, he pursued an engineering degree to further this interest. Under his leadership, Moon Technolabs has helped numerous brands establish their online presence and he has also launched an invoicing software that assists businesses to streamline their financial operations.

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