AWS vs GCP vs Azure: AI/ML Platform
Google Cloud ML Engine
• Amazon SageMaker automatically configures and optimizes TensorFlow, Apache MXNet, PyTorch, Chainer, Scikit-learn, SparkML, Horovod, Keras, and Gluon.
• Commonly used machine learning algorithms are built-in and tuned for scale, speed, and accuracy with over a hundred additional pretrained models and algorithms available in AWS Marketplace.
• You can also bring any other algorithm or framework by building it into a Docker container.
• Cloud Machine Learning Engine: Massively scalable managed service for training ML models & making predictions.
• Enables apps/devs to use Tensorflow on datasets of any size
Supports online & batch predictions, prioritising latency (online) & job time (batch) • Or download models & make predictions anywhere: desktop, mobile, own servers
Azure ML Studio
• Azure Machine Learning: End to End Data Science Solution • Uses PyTorch, Tensorflow and Keras • Multiple Components: • ML Workbench • ML Experimentation Service • ML Model Management Service • ML Libraries for Spark • Visual Studio Code tools for AI
• HyperTune automatically tunes model hyper parameters to avoid manual tweaking Source: Google Cloud