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AI Platforms and Engines

University of Wisconsin_Madison_031422A
[University of Wisconsin-Madison]

- Overview

Many are the time when businesses have workflows that are repetitive, tedious and difficult which tend to slow down production and also increase the cost of operation. To bring down the cost of production, businesses have no option rather than automate some of the function to cut down the cost of production. 

By digitizing repetitive tasks, an enterprise can cut cost on paperwork and labor which further eliminates human error thus boosting efficiency leading to better results. For businesses to gain from the above benefits, they must choose the right automation tools. 

Automating processes involves employing AI platforms that can support the digitization process and deliver the same or better results that human being would have achieved.

Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. AI platforms is defined as some sort of hardware architecture or software framework (including application frameworks), that allows software to run.

AI application also involves the use of expert systems such as speech recognition, and machine vision. AI platforms can be classified as either weak AI/narrow AI which is generally meant for a particular task or strong AI also known as artificial general intelligence which can find solutions for unfamiliar tasks.

Please refer to the following for more information:


- AI Platforms

Enterprises are adopting AI and machine learning (ML) for various use cases, which has risen the demand for AI engines and open-source AI platforms that can be used to develop intelligent applications and tools. Such apps and tools help them automate the repetitive, tedious and difficult tasks that can affect productivity and cost of operation. 

AI Platform makes it easy for ML developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, AI Platform’s integrated tool chain helps you build and run your own machine learning applications.

AI platforms (also called machine learning platforms or data science platforms) allow users to analyze data and process data, build machine learning models, deploy and maintain these models.  

To be categorized as an AI platform, a product must be able to:

  • Work with a variety of use cases, should not be specific to one industry
  • Allow users to build, deploy and maintain models that power business decisions.


AI platforms involve the use of machines to perform the tasks that are performed by human beings. The platforms simulate the cognitive function that human minds perform such as problem solving, learning, reasoning, social intelligence as well as general intelligence. 


- Top AI Platforms

To build new AI projects or take existing projects to the next level, there is a wide range of AI engines and open-source AI development platforms available for developers. These AI engines can be used to build several AI applications, like a personal assistant, chatbot, to connect to smart home, control devices, and more. Today, there are several options available for developers to build their own AI project.  

Following are some of top AI platforms and engines for businesses to develop AI-based applications:  

  • Google Cloud AI Platform: A user-friendly platform that supports popular frameworks like TensorFlow and Scikit-learn. It also has built-in algorithms for various tasks. 
  • TensorFlow: An open-source platform that uses Python for numerical computation and machine learning. It was created by Google and is one of the most popular AI software products. 
  • Infosys Nia: An AI platform developed by Infosys that uses machine learning and natural language processing to help businesses automate their workflows. 
  • IBM Watson: An AI-powered platform that helps developers create conversational chatbot experiences.  It's designed to help businesses develop, deploy, and manage AI-powered applications and services. 
  • Azure Machine Learning Studio: An AI application that provides systems with the ability to automatically learn and improve from experience. 
  • Spacemaker AI: A commercial platform that uses AI to help architects, urban planners, and developers make better decisions faster. 
  • DataRobot: An AI-powered platform that automates the process of building, deploying, and maintaining machine learning models. It has an intuitive interface and automation features that allow businesses to use AI without needing extensive machine learning expertise. 
  • Salesforce Einstein: An AI-powered platform that helps marketers create personalized experiences for customers. It can be used to automate customer segmentation, generate insights about customer behavior, and optimize campaigns. 


- AI Engines: Meeting the Computing Needs of Next-generation Applications

In many dynamic and evolving markets, such as 5G cellular, data center, automotive and industrial, applications are driving ever-increasing improvements in computing acceleration while maintaining energy efficiency. 

Since Moore's Law and Dennard scaling no longer follow their traditional trajectories, simply moving to the next generation of silicon nodes cannot deliver the benefits of lower power and cost and better performance that previous generations did. 

In order to cope with the non-linear growth of next-generation application requirements such as wireless beamforming and machine learning inference, a new innovative processing technology - AI engine - is emerging.


[More to come ...]

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