SageMaker : Good option for 'fail early learn fast' in ML and DL
Rating: 10 out of 10
IncentivizedUse Cases and Deployment Scope
We are using the SageMaker service from AWS for POC, and to build the final model on the large dataset of healthcare domain under the R&D department. SageMaker also provides hosting functionality, so that we can host a created model for the end-level application which is accessible through a simple API call from any application.
Pros
- Provided an instance of Jupyter notebook for development script, which made it very easy to manage and develop any script.
- Our system is cloud-based, and we are charged only for what we use and how long we use it.
- We can choose multiple servers for Training, without any headache of distribution.
- Most of the libraries are supported.
- All training, testing, and models are stored on S3, so it's very easy to access whenever we require it.
Cons
- 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.
Likelihood to Recommend
Well suited scenarios:
- For quick POC of ML and DL.
- To train a model on a large dataset using multiple servers.
- To host a model to be used by multiple applications.
- For data analysis tasks.
- For a data scientist who has less of a programming background.
Vetted Review
1 year of experience