The three big cloud providers, specifically Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), want developers and data scientists to develop, test, and deploy machine learning models on their clouds. It’s a lucrative endeavor for them because testing models often need a burst of infrastructure, and models in production often require high availability.
These are lucrative services for the cloud providers and offer benefits to their customers, but they don’t want to compete for your business only on infrastructure, service levels, and pricing. They focus on versatile on-ramps to make it easier for customers to use their machine learning capabilities. Each public cloud offers multiple data storage options, including serverless databases, data warehouses, data lakes, and NoSQL datastores, making it likely that you will develop models in proximity to where your data resides. They offer popular machine learning frameworks, including TensorFlow and PyTorch so that their clouds are one-stop shops for data science teams that want flexibility. All three offer Modelops, MLops, and a growing number of capabilities to support the full machine learning life cycle.
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