Amazon SageMaker vs. AWS Batch

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

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

• 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

• Easy to orchestrate and trigger jobs
• No time limit issues like lambda
• Multiple Jobs can be run in same single compute and job queue
• JOb queue can queue up task for parralled or serialization

Incentivized

• 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

• 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

Key advantages include cost-effectiveness through dynamic resource provisioning and the use of spot instances. It auto-scales to meet workload demands, allowing easy job submission via the AWS Management Console or SDKs. It integrates seamlessly with other services like S3 and CloudWatch. It features automatic retries for failed jobs. It allows for a custom computing environment tailored to specific needs

Incentivized

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 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