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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 24% 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.

Buyer's Guide

Download the Data Science Platforms Buyer's Guide including reviews and more. Updated: January 2022

Amazon SageMaker Customers

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

Amazon SageMaker Video

Amazon SageMaker Pricing Advice

What users are saying about Amazon SageMaker pricing:
"The support costs are 10% of the Amazon fees and it comes by default."

Amazon SageMaker Reviews

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PankajUrmaliya
Lead Data Scientist at a tech services company with 201-500 employees
Real User
Top 20
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.
Jaison Jose
Cloud Architect & Support Service Delivery Manager at Almoayyed Computers
Reseller
Top 20
Straightforward setup, scalable, and the technical support is good
Pros and Cons
  • "The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
  • "AI is a new area and AWS needs to have an internship training program available."

What is our primary use case?

We are a solution provider that is concentrating on migrating our customers from on-premises to the cloud, and Amazon SageMaker is one of the products that we implement for our customers.

SageMaker is an AI platform, and I have been working on creating a solution that uses SageMaker and DeepLens to recognize people for access control. It will automatically log people who are coming and leaving. The second use case that we are working on is a system that recognizes cars by reading license plates and then opening a gate automatically to let them into the parking area.

AI, in general, has not yet been heavily used in this region so I am working on three or four use cases.

What is most valuable?

The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases. As it is, we can start to use it directly.

What needs improvement?

AI is a new area and AWS needs to have an internship training program available. This is one place where I see this solution lagging. There is high-level training available, but when you consider that people have been working with Windows, Linux, and various applications for the past 20 years, they know those products inside and out. SageMaker, on the other hand, is a completely new tool. It can be very hard to digest.

AWS needs to provide more use cases for SageMaker. There are some, but not enough. They should collect or create more use cases and then distribute them free of charge to the customers.

I would like to see a more graphical, low-code interface that can be used to customize SageMaker.

For how long have I used the solution?

We have just begun to provide services using SageMaker.

What do I think about the scalability of the solution?

This solution is completely scalable. 

How are customer service and technical support?

I have been in contact with Amazon technical support in the past, but not for SageMaker. I have between 50 and 70 customers and I have worked with Amazon support on multiple cases. I am quite happy with it. It is not expensive and the service is great.

The value you get for paying from Amazon support is great. They are ready to work with me to resolve my issues.

How was the initial setup?

The initial setup is straightforward. People with level-one training can start using it.

It usually takes about one hour to deploy, although the length of time and the number of people required are dependent on the complexity of the use cases and the environment.

What's my experience with pricing, setup cost, and licensing?

The business support costs 10% of the Amazon utility spend

What other advice do I have?

Myself and certain people in my team have just begun the training. There is an eight-hour training video to assist with learning how to use this solution.

I would rate this solution an eight 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: My company has a business relationship with this vendor other than being a customer: Reseller
Find out what your peers are saying about Amazon, Databricks, Microsoft and others in Data Science Platforms. Updated: January 2022.
564,143 professionals have used our research since 2012.
Consultant at a tech services company with 501-1,000 employees
Consultant
Top 20
Great for automating pipelines and creation of API endpoints
Pros and Cons
  • "Allows you to create API endpoints."
  • "Lacking in some machine learning pipelines."

What is our primary use case?

Our primary use case for SageMaker is for developing end to end machine learning solutions and ready solutions for things such as computer vision or speech recognition or speech to text. It's basically providing off-the-shelf solutions. Our customers are generally medium to enterprise size companies. We're a partner of Amazon.

What is most valuable?

The most valuable feature of the solution is that it allows you to create API endpoints and that saves a lot of time for data scientists. 

What needs improvement?

The product has come a long way and they've added a lot of things, but in terms of improvement I would like to probably have features such as MLflow embedded into it.

Additional features I would like to see would include, as mentioned, MLflow and ML Pipelines which are more of a feature rich support of machine learning pipelines as well as scheduling machine learning pipelines, and visualization of machine learning pipelines.  

For how long have I used the solution?

I've been using this solution for about a year.

What do I think about the stability of the solution?

The solution is quite stable. 

What do I think about the scalability of the solution?

The solution is hosted on Amazon so it's quite scalable.

How are customer service and technical support?

The documentation is good so I haven't needed to use technical support. 

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

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. 

How was the initial setup?

Initial setup is quite straightforward. 

What's my experience with pricing, setup cost, and licensing?

The pricing for the Notebook endpoints is a bit high, but generally reasonable. 

What other advice do I have?

I think for anyone using SageMaker it will help automate pipelines, and make it easier than doing the process manually. For anyone already on the AWS platform, they should definitely make use of it.

I would rate this product an eight out of 10. 

Disclosure: My company has a business relationship with this vendor other than being a customer: partner