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Amazon SageMaker OverviewUNIXBusinessApplication

Amazon SageMaker is #9 ranked solution in top Data Science Platforms. PeerSpot users give Amazon SageMaker an average rating of 8 out of 10. Amazon SageMaker is most commonly compared to Databricks: Amazon SageMaker vs Databricks. The top industry researching this solution are professionals from a computer software company, accounting for 22% of all views.
What is Amazon SageMaker?

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon SageMaker was previously known as AWS SageMaker, SageMaker.

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Amazon SageMaker Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Amazon SageMaker Video

Amazon SageMaker Reviews

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PankajUrmaliya - PeerSpot reviewer
Lead Data Scientist at a tech services company with 201-500 employees
Real User
Good deployment and monitoring features, but the interface could use some improvement
Pros and Cons
  • "The deployment is very good, where you only need to press a few buttons."
  • "Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

What is our primary use case?

This is a solution that we have provided to one of our clients.

The client is in the business of consumer goods and they wanted to get accurate demand forecasts to evaluate performance of campaigns and optimize inventory.

The solution is an ensemble of regression models and is deployed on their AWS Cloud and all of the data is on Amazon Redshift. 

What is most valuable?

The deployment is easy and good. The documentation is pretty good also.

Integration with other AWS services is seamless.

What needs improvement?

The interface and the IDE could have some improvement. UX isn't bad but could be better.

Orchestration of the ML flow can be made easier (like ETL etc.)

Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier.

Adding certain AI functionalities similar to what DataRobot or Azure AI has would be really great.

For how long have I used the solution?

I have been using Amazon SageMaker for four to five months.

What do I think about the stability of the solution?

This is a stable solution. We haven't seen any glitches as of yet.

What do I think about the scalability of the solution?

It is scalable to a degree. We have used several open data sources and have found that for small data, it works well. However, as the volume of data increases, there are issues with respect to scalability.

In general, I would say that for small to medium volumes of data, this solution works well. For bigger data, there is room for improvement. 

We have a team of five people who are using SageMaker.

How are customer service and technical support?

We did not need to contact technical support because the documentation is good and we have in-house expertise.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

I did not have trouble with the initial setup and I don't think that it was very complex. Overall, I would say that it is good.

What about the implementation team?

We have a few experts here who helped with the implementation. The deployment took about a week to get everything ready.

Two people are suitable for maintenance and support.

What other advice do I have?

My advice to anybody who is considering this solution is to think about using multiple cloud services. This solution is good but for complex business problems and big data, it gets a bit trickier. In terms of deployment, it is a clear winner.

From the cost point of view, it's relatively on the higher side.

Overall, there are a few improvements that I want but SageMaker is pretty good.

I would rate this solution a seven out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Buyer's Guide
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Updated: April 2022
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Buyer's Guide
Download our free Data Science Platforms Report and find out what your peers are saying about Amazon, Databricks, Microsoft, and more!