Amazon Machine Learning vs FloydHub: What are the differences?
Developers describe Amazon Machine Learning as "Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology". This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure. On the other hand, FloydHub is detailed as "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 Machine Learning and FloydHub belong to "Machine Learning as a Service" category of the tech stack.
What is Amazon Machine Learning?
What is FloydHub?
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Why do developers choose Amazon Machine Learning?
Why do developers choose FloydHub?
What are the cons of using Amazon Machine Learning?
What are the cons of using FloydHub?
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What tools integrate with Amazon Machine Learning?
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.
Easy way to scale data science projects from laptop to the cloud to utilize GPU processing power and access key data training sets without downloading to your local machine.