Episode description
In Cloud Provider Comparisons, we take a look at the same cloud services across the three major public cloud providers – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). In this video, we focus on artificial intelligence (AI) and machine learning (ML). We compare the major ‘building block’ services (those you can use without having to know much about ML), explainability and bias services, and ML infrastructure and platforms. If you’re curious about how the AI and ML services of AWS, Azure and Google Cloud Platform match up, join ACG Senior Training Architect Scott Pletcher and watch on to find out more!
Timestamps:
0:00 Introduction
0:52 What is AI and ML (and what’s the difference)?
2:41 What this episode will cover
3:33 Machine learning building block services
7:56 Machine learning platforms
10:20 Machine learning infrastructure
12:30 Machine learning explainability and bias
Here’s some more detail on the AI and ML services we’ll cover in this episode:
ML building blocks:
- Speech to text: Amazon Transcribe, Azure Speech to Text, GCP Speech to Text
- Text to speech: Amazon Polly, Azure Text to Speech, GCP Text to Speech
- Chatbots: Amazon Lex, Azure Language Understanding, GCP DialogFlow
- Language translation: Amazon Translate, Azure Translator, Google Cloud Translation
- Text analytics: Amazon Comprehend, Azure Text Analytics, GCP Natural Language
- Document analysis: Amazon Textract, Azure Form Recognizer, GCP Document AI
- Image & video analysis: Amazon Rekognition, Azure Computer Vision and Video Indexer, GCP Vision AI and Video AI
- Anomaly detection: Amazon Lookout family and Fraud Detector, Azure Anomaly Detector and Metrics Advisor, GCP Cloud Inference
- Personalization: Amazon Personalize, Azure Personalizer, GCP Recommendations AI
ML platforms:
- ML tools (Jupyter Notebook) and learning frameworks (Tensorflow, MXNet, Keras, PyTorch, Chainer, SciKit Learn)
- Guided model development: Amazon SageMaker Autopilot, Azure Automated ML and Designer, GCP AutoML
- Full ML workbench: Amazon SageMaker Studio, Azure Machine Learning Notebooks, GCP AI Platform
- MLOps: Amazon Sagemaker MLOps, Azure MLOps, GCP Pipeline
ML explainability and bias: Amazon SageMaker Clarify, Azure Responsible ML, GCP AI Explanations
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Let us know in the comments what other cloud services you’d like to see us cover!
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Episode resources:
- Scott’s Introduction to Machine Learning course: https://acloudguru.com/course/introduction-to-machine-learning
- Machine Learning for Absolute Beginners course: https://acloudguru.com/course/machine-learning-for-absolute-beginners
- Getting Started with Azure Machine Learning Studio course: https://acloudguru.com/course/getting-started-with-azure-machine-learning-studio
- Introduction to Jupyter Notebooks course: https://acloudguru.com/course/introduction-to-jupyter-notebooks
- AWS Certified Machine Learning Speciality course: https://acloudguru.com/course/aws-certified-machine-learning-specialty
- Google Cloud AI Services Deep Dive course: https://acloudguru.com/course/google-cloud-ai-services-deep-dive
Series description
In Cloud Provider Comparisons, we explore and compare the same cloud service across the three major public cloud providers - Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
