Amazon SageMaker vs. Cloudera Data Platform

Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.

Incentivized

I have seen that Cloudera Data Platform is well suited for large batch processes. It works really well for our indication analyses that are performed by the actuaries. I feel that rapid streaming operations may be a situation where additional technology would be needed to provide for a robust solution.

Incentivized

• Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
• Customization and easy to alter and change.
• If you already are an Amazon user, you do not need to transition over to another software.

Incentivized

• Scales
• Highly available

Incentivized

• The UI can be eased up a bit for use by business analysts and non technical users
• For huge amount of data pull from legacy solutions, the platform lags a bit
• Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized

Incentivized

• Constantly changing costs
• Log visibility

Incentivized

We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.

Incentivized

Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.

Incentivized

IBM's offering of the Cloud Pak for Data has been a moving target and difficult to compare to Cloudera Data Platform. We have implemented our solution on Amazon Web Services, which appears to be supported by IBM at this point, but the migration would be very expensive for us to endeavor.

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

• Reduced operational costs
• Speed to market

Incentivized