Amazon SageMaker vs FloydHub: What are the differences?
Amazon SageMaker: Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale; FloydHub: Heroku for Deep Learning. Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.
Amazon SageMaker and FloydHub can be categorized as "Machine Learning as a Service" tools.
What is Amazon SageMaker?
What is FloydHub?
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Why do developers choose Amazon SageMaker?
Why do developers choose FloydHub?
What are the cons of using Amazon SageMaker?
What are the cons of using FloydHub?
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What tools integrate with FloydHub?
Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.
Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.
Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.
Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.