Amazon SageMaker vs. AWS Batch

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

More appropriate if you have a tech group that can use more of the AWS Batch rather than one or 2 things. It works great for me, but there was a huge learning curve the first week of using it. Now, I love it - and I hope to dig deep into other parts not just S3.

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

• S3 amount of files
• Easy to share files to other sites
• Like that the files and folders can be open and public or completely private
• I like that you can have double or mirroring in different Data Centers for Data Recovery

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

• Jobs monitoring dashboards are not matured
• Documentation and support is something which can be improved
• Sometime i faced the slow response or slow in performance i would say

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

We wanted to start everything on a scale & with fewer resources to manage the underlying infrastructure.

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

• Overall over business is able to save the cost
• Saved our times to improve the existing process
• Able to integrate with other applications as well, so that is plus point

Incentivized