When NOT to use Amazon Sagemaker

Hey Friends ✋
So I hope you liked my previous article on why we can move towards Amazon Sagemaker, but as they say, you can use sugar everywhere.
Similarly, in this article we are going to discuss some scenarios, where we can NOT use Amazon Sagemaker. Or I should rephrase it as where Amazon Sagemaker won't be a viable option.

The problems we discussed in the past article though represent a vast majority of ML projects, there are certainly few scenarios where this might not be a valid option.

Scenarios

1. Sagemaker, expects you to code your data engineering and analysis needs(in your choice of language).
2. The entire orchestration of infrastructure and transition from one phase to another though happens with few lines code; definitely needs a small amount of code.
3. For professionals, who prefer a drag and drop (absolutely no code) solutions for the data engineering and modeling phase, Azure ML studio would be a more ideal choice.
4. Both Azure ML Studio and AWS Sagemaker are great platforms for developing ML solutions but are targetted for a completely different set of users.

Conclusions

AWS Sagemaker has been a great deal for most data scientists who would want to accomplish a truly end-to-end ML solution. It takes care of abstracting a ton of software development skills necessary to accomplish the task while still being highly effective and flexible and cost-effective. Most importantly, it helps you focus on the core ML experiments and supplements the remainder necessary skills with easy abstracted tools similar to our existing workflow.