I have used Databricks before. From a feature perspective, Databricks is better than SageMaker. The user experience of Databricks is much better. SageMaker’s advantage is its cost. The data is already on AWS S3. It’s less of a hassle. SageMaker is convenient. Databricks is like a MacBook. I can still work with SageMaker, but it is not as pretty, and the flow is not as natural as on Databricks.

We were using Azure’s tool before. We switched to Amazon SageMaker because it allows us to sell it to larger institutional clients. AWS is more prevalent in the broader institutional segment.

Our Infotech company comes under shared services. So, technically, we provide solutions for every other department.

Plus, we are working with AWS. Plus, we have, we have in-house tools that we develop. We also use Microsoft tools like Teams, Office, and SharePoint, as well as EDMS.

We also use SageMaker and Databricks.

I have previously worked with Google Cloud Platform (GCP). We first moved from GCP to Databricks because it was cost-efficient. Now, we are moving from Databricks to Amazon SageMaker to see whether it serves our purpose and is beneficial with respect to cost and time.

Along with Amazon SageMaker, I use other services from AWS, like AWS Glue, Athena, Redshift, SQS, SNS, and Airflow.

I researched Amazon SageMaker on my own.

SageMaker was the first cloud solution I've used but there are other products, such as Databricks or Google and Azure that have similar products. There are common features with all these products but I'd say that SageMaker has more features than Databricks. Azure has other features in addition to Databricks, but SageMaker has provided everything. 

I have previously used Databricks. 

I have also used the Microsoft Azure Machine Learning Studio and Databricks, and the interface is a little better with these solutions. The Microsoft solution is really good in terms of user experience.

When it comes to deployment and integrating with cloud services, Amazon SageMaker is better as AWS.