Big Three Cloud Giants Compete in AI Education, Training, and Certification

Top cloud provides focus on AI educational and training initiatives.

The three leading cloud giants, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), are competing for supremacy in the artificial intelligence (AI) space, with a particular focus on educational and training initiatives.

This rivalry gained significant momentum following the introduction of ChatGPT, a sophisticated chatbot developed by Microsoft's partner, OpenAI, last year. Microsoft, backed by an investment exceeding $10 billion, gained an early advantage in the race. This development prompted Google to take urgent action, resulting in what they described as a "code red."

Given that both Microsoft and Google have substantial stakes in search, which is increasingly dominated by AI, they are expected to intensify their efforts across various AI domains, particularly in the area of education and training. For instance, Microsoft's AI-powered "New Bing" search engine is competing with Google's AI-driven Bard search offering.

Here's a look at what each cloud platform is doing in the AI education/training space.


While the company isn't as likely to be disrupted by AI as much as search-invested Google and Microsoft, it still has jumped on the AI education/training bandwagon in a big way with a plethora of opportunities. Here are summaries of some of the main ones.

  • AWS Training and Certification Blog: ChatGPT debuted on Nov. 30, 2022. Less than two weeks later, the AWS Training and Certification Blog published the first of many "New courses and updates from AWS Training and Certification" posts in the AI category. Subsequent posts have been published for each month after, with the May 11 post (for April) listing new opportunities for Amazon Comprehend (natural language processing) and many more. Thus, the blog serves as a one-stop-shop to keep abreast with new education/training offerings.
  • AWS Training and Certification Site: This "Build your future in the AWS Cloud" site features three main categories on the home page: Cloud Essentials, Architecting and Machine Learning. Offerings showcased for the latter currently include three courses: The Machine Learning Pipeline on AWS; Exam Readiness: AWS Certified Machine Learning - Specialty; and Deep Learning on AWS. All courses can be seen here.
  • AWS Skill Builder: This "online learning center" offers free learning content along with individual and team subscriptions. The Machine Learning section features a "set of on-demand courses will help grow your technical skills and learn how to apply machine learning (ML), artificial intelligence (AI), and deep learning (DL) to unlock new insights and value in your role. Learning Plans can also help prepare you for the AWS Certified Machine Learning - Specialty certification exam." Speaking of the latter ...
  • AWS Certified Machine Learning - Specialty: This exam "is intended for individuals who perform a development or data science role and have more than one year of experience developing, architecting, or running machine learning/deep learning workloads in the AWS Cloud."
  • Get Started with Machine Learning: This resource aims to help users unlock their ML skills and career potential with deep dive coursework, hands on tutorials, and more. Spotlighted courses include Practical Data Science Specialization, ML Essentials for Business and Machine Learning University. Speaking of the latter, ....
  • Machine Learning University: This offering provides self-service machine learning training from Amazon's own scientists. "The courses offered from Machine Learning University are the same courses used to train Amazon's own developers on machine learning fundamentals. Getting started with MLU is easy and provides learners with a self-paced and flexible learning structure."

Google Cloud Platform

As noted, Google started a massive "code red" AI catch-up initiative after the debut of ChatGPT, quickly spinning up an experimental Bard AI-powered search experience. Since then, it has basically played a tit-for-tat AI game with new updates and announcements. Here's a summary of the company's education/training efforts.

Machine learning and AI: This is a Google Cloud training resource titled Machine learning and artificial intelligence . The site features a Data Scientist / Machine Learning Engineer learning path, about which the company says: "A Data Scientist models and analyzes key data to continually improve how businesses utilize data. Data Scientists aim to make accurate predictions about the future using in-depth data modeling and deep learning." It includes courses such as:

  • Big Data & Machine Learning Fundamentals
  • Machine Learning on Google Cloud
  • Advanced Machine Learning with TensorFlow on Google Cloud Platform
  • MLOps (Machine Learning Operations) Fundamentals
  • ML Pipelines on Google Cloud

Skill badges that can be earned via those courses include:

  • Perform Foundational Data, ML, and AI Tasks in Google Cloud
  • Build and Deploy Machine Learning Solutions on Vertex AI
  • Create Conversational AI Agents with Dialogflow CX

Google AI/Build: "What are you trying to do with AI today?" asks this site, which under the Learn ML category lists 14 homegrown and third-party courses including:

  • Basics of machine learning with TensorFlow (TensorFlow)
  • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera)
  • Intro to TensorFlow for Deep Learning (Udacity)
  • DeepLearning.AI TensorFlow Developer Professional Certificate (Coursera)
  • Introduction to Machine Learning Problem Framing (Google Foundational)
  • Intro to Fairness in Machine Learning (Google Foundational)
  • Kaggle Learn (Kaggle)
  • Machine Learning Guides (Google Guides)
  • Machine Learning Crash Course with TensorFlow APIs (Google Foundational)

Grow with Google: "Here's your guide to learn AI & machine learning," says this site. It begins with resources to "start with:"

  • Master machine learning with Google experts
  • Learn the basics of AI
  • Make sense of artificial intelligence
  • Explore AI and learn from Google's experts

followed by resources to "then try:"

  • Get certified in TensorFlow
  • Learn to program Neural Networks with TensorFlow
  • Explore your ML career in Google Cloud

Introduction to Generative AI: This Google Cloud Skills Boost resource is explained like this: "This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps. This course is estimated to take approximately 45 minutes to complete."

Seven new no-cost generative AI training courses to advance your cloud career: This May 18 post under Training and Certifications features textual resources as well as videos, with the latter including:

  • Introduction to Generative AI
  • Introduction to Large Language models
  • Generative AI on Google Cloud

Generative AI learning path: This resource lists courses mapped to those above videos, along with many more. A sampling includes:

  • Introduction to Image Generation
  • Encoder-Decoder Architecture
  • Attention Mechanism
  • Transformer Models and BERT Model
  • Create Image Captioning Models
  • Introduction to Generative AI Studio
  • Generative AI Explorer - Vertex AI (a "Quest")

Microsoft Azure

As mentioned, Microsoft gained a clear lead in the advanced AI space -- especially generative AI -- thanks to its partnership with OpenAI. Following are summaries of what the company is doing for learning AI and machine learning in the cloud.
Microsoft Learn education/training: For Azure education/training opportunities in the Microsoft Learn space, the offerings for the "AI Engineer" role include:

  • Create a regression model with Azure Machine Learning designer (module)
  • Use Automated Machine Learning in Azure Machine Learning (module)
  • Create a clustering model with Azure Machine Learning designer (module)
  • Create a classification model with Azure Machine Learning designer (module)
  • Microsoft Azure AI Fundamentals: Explore visual tools for machine learning (learning path)
  • Introduction to machine learning (module)
  • Build classical machine learning models with supervised learning (module)
  • Create machine learning models (learning path)
  • Introduction to data for machine learning (module)
  • Refine and test machine learning models (module)
  • Train and understand regression models in machine learning (module)
  • Explore security concepts in Azure Machine Learning (module)
  • Autonomous AI design architect (learning path)
  • Machine Teaching for Autonomous AI (learning path)
  • Autonomous AI implementation engineer (learning path)
  • Build a Web App with Refreshable Machine Learning Models (module)

Another 32 are offered for the role of Data Scientist.

Microsoft Learn Certifications: Certs for the role of "AI Engineer" include:

  • Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
  • Exam AI-900: Microsoft Azure AI Fundamentals
  • Microsoft Certified: Azure AI Engineer Associate
  • Microsoft Certified: Azure AI Fundamentals

Certs for the role of "Data Scientist" include some of those also, along with:

  • Exam DP-100: Designing and Implementing a Data Science Solution on Azure
  • Microsoft Certified: Azure Data Scientist Associate
  • Microsoft Certified: Customer Data Platform Specialty
  • Exam MB-260: Microsoft Customer Data Platform Specialist

Advancements in the AI/ML space -- especially generative AI -- are currently running amok, with one milestone breakthrough followed by another. So, stay tuned to see how the cloud giants update their AI education/training offerings to keep pace.

About the Author

David Ramel is an editor and writer for Converge360.