ML usecase feasibility for Databricks ML Vs AWS Sagemaker/Azure ML

[Saurabh707344]

What complexity of ML models are feasible to be created in Databricks ML and further that we have to rely on AWS Sagamaker or Azure ML ?

Do we have clear segragation around it by ML usecases ?

[Anonymous]

Hi @Saurabh Singh​ 

Great to meet you, and thanks for your question!

Let's see if your peers in the community have an answer to your question. Thanks.

[shyam_9 · Accepted solution]

Hi @saurabh707344,

In Databricks, you can handle everything end to end. Creating, and testing a model to deploying and serving it through a endpoint. You can also, track the experiments and compare them and maintain a CI/CD. You can always contact the sales/accounts team from Databricks, they have more details based on your usecase. https://www.databricks.com/company/contact

[corvo]

In Databricks, your usecase can be solved by the notebooks provided here in databricks. There is no dependency on AWS sagemaker directly. 
All the model traiing and deployement that can be done in sagemaker, is supported via databricks as well.