In essence, AWS, Microsoft Azure and Google Cloud Platform offer pretty similar capabilities revolving around flexible compute, storage and networking. What they have in common are the common elements of a public cloud: self-service and instant, autoscaling, provisioning as well as security, compliance and identity management features.
All three platforms have launched services and tools targeted at advanced technology areas like the Internet of Things (IoT) and serverless computing (Lambda for AWS, Functions with Azure and Google). Machine learning has also been a rampant area in the cloud computing technological escalation lately.
AWS launched SageMaker in 2017 to encourage the adoption of machine learning. The platform also has a broad set of ready-made machine learning services for use cases like image recognition, text-to-speech learning models, and Alexa's engine.
Microsoft's Azure Machine Learning lets developers write, test and deploy algorithms, along with accessing a marketplace for off-the-shelf APIs.
Google offers a one-stop-shop AI platform, allowing machine learning engineers to build and deploy models based on its popular open-source TensorFlow deep learning library.
The recent craze around containers is considered, with all three providers offering managed services around superior container services like Kubernetes.