Amazon SageMaker vs. MLReef

It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

Incentivized

Works well if you have to involve different roles in different organizations in a project. Less suited when you have a complex system of custom developed tools

• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

Incentivized

• Helps us to take on more client projects
• Can be used by data analysts as well as casual users

• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Incentivized

• Out of the box support for major cloud vendors

Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

Incentivized

• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

Incentivized

• We can handle 4 to 6 times more projects at the same time with our team
• We stay engaged with our customers well beyond the project duration