Amazon SageMaker vs wise.io: 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; wise.io: Machine Learning as a Service and Big Data Analytics. Wise.io builds machine intelligence products that make it easy for companies to derive actionable insight from their greatest corporate resource: their data.
Amazon SageMaker and wise.io belong to "Machine Learning as a Service" category of the tech stack.
Some of the features offered by Amazon SageMaker are:
- Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
- Train: one-click training, authentic model tuning
- Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling
On the other hand, wise.io provides the following key features:
- Use Wise.io for: Fraud detection, Intelligent sensors, Ad Targeting & Personalization, Genomics, Business Analytics, Finance, Healthcare, Sentiment Analysis
- Dead simple machine learning.- Our intuitive, easy-to-use platform for machine learning enables anyone to build and deploy models with a few simple clicks.
- A data science marketplace.- With the feature marketplace, we provide companies access to an expansive knowledge base.
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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.