AI Platforms To Use for Your AI Project in High School
Written by Samiyah Shaikh
Hello everyone! My name is Samiyah Shaikh, I’m a freshman in college at UC Santa Cruz studying Computer Science. I was a participant in the Inspirit AI program during my junior year of high school, and I’m excited to be back in the program as an AI Ambassador! Today, I’ll be discussing how to create your own AI projects, particularly, the platforms that are readily available to use to create AI or machine learning applications.
Platforms for your AI Projects
Artificial intelligence is a groundbreaking technology that’s already being utilized in many services and applications today and is said to exponentially increase the efficacy of existing technology.
As fellow AI enthusiasts that are part of the Inspirit AI Ambassadors program, it’s exciting to see the variety of opportunities and solutions that AI has to offer going forward. Thus, being able to expand our knowledge of AI perhaps through individual projects and work can further help us build our repertoire of skills and gain insight into skills that will be useful in the tech industry going forward. Here’s a list of some popular AI platforms that can be utilized to create one’s own AI projects!
Google AI Platform
The Google AI Platform is an easy-to-use platform for individuals looking to create ML projects. There’s an integrated tool chain in this application that helps you build and run your projects, and you have access to other technologies through this platform, such as TensorFlow, TPUs, and TFX. Some prominent features of this application include cloud-based machine learning, customer sentiment analysis, and spam detection.
TensorFlow
TensorFlow is an open-source software library that is often used to compute numerical calculations with the use of data flow graphs. There are specific nodes in these graphs that represent specific math operations, and the entire graph’s edges represent the tensors that are communicated between the nodes of the graph.
This makes for a flexible structure and allows users to use more than one CPU or GPU in their computations, regardless of whatever method they may choose (desktop, mobile device, server, etc.). Thus, this platform has quite deep flexibility and portability embedded into it. This platform was created by engineers on the Google Brain Team within Google’s Machine Intelligence research organization, and their purpose was to create a platform that could aid in creating machine learning applications and engage in deep neural networks research.
Microsoft Azure
Microsoft Azure is an AI platform that utilizes cloud-based advanced analytics to help simplify machine learning for businesses. It’s a useful tool particularly for businesses looking to apply machine learning models to their businesses; models created through this application can be completed and deployed in a matter of minutes, making it a platform very prone to productivity and efficiency. Work completed on Microsoft Azure can also be published in the Machine Learning Marketplace for the community to see.
DialogFlow
DialogFlow, previously known as API.AI, is an AI platform that features natural language processing tools, which can be used to develop bots, applications, services, etc. Users can have access to predefined knowledge packages that have been collected over several years to use, such as flight schedules, weather, encyclopedic data, etc. This data can be used as training or testing data in predictive modeling too. This platform is best for building AI-based voice and text conversational apps.
Summary
Overall, here are some of the most well-known AI platforms to use to create AI applications. There are many other AI platforms out there as well, each with its unique purpose and included features. Many of these platforms do require a solid knowledge of programming concepts and experience with coding in a variety of languages such as Python, R, and others, as well as proficient knowledge of basic AI concepts, such as machine learning and neural networks.
Thus, it’s a good idea to practice coding in at least 2-3 languages and gain proficiency in them before delving into one’s own AI project, as well as reading about basic AI topics. Some websites that offer useful resources and additional coding practice are Codecademy and Leetcode, both of which can help you learn a variety of different coding languages and expand your skills as a programmer.
Sources:
Top 18 artificial intelligence platforms in 2021 - reviews, features, pricing, comparison. PAT RESEARCH: B2B Reviews, Buying Guides & Best Practices. (2021, September 28). Retrieved October 22, 2021, from https://www.predictiveanalyticstoday.com/artificial-intelligence-platforms/.
Image free for commercial use from Pixabay
