Amazon SageMaker Reviews

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.

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.

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.

We have just begun to provide services using SageMaker.

This solution is completely scalable. 

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.

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.

The business support costs 10% of the Amazon utility spend

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.

We use the solution as an OCR to extract text from documents, images, PDFs, etc. 

The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc. 

I am impressed with the tool's text extraction and its accuracy. 

The solution needs to be cheaper since it now charges per document for extraction. 

I have been using the solution for five years. 

I would rate the tool's stability a nine out of ten. 

I would rate the tool's scalability a nine out of ten and we use it once a week. 

The support's response to tickets is slow. 

Neutral

I would rate the product's deployment a nine out of ten since it's straightforward. The deployment gets completed within 20 minutes. You need one IT person and a developer to handle the deployment and maintenance. 

We have seen ROI with the tool's use. 

I would rate the solution's price a ten out of ten since it is very high. 

I recommend the tool for document processing and would rate it an eight out of ten. 

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.

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. 

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.  

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

The solution is quite stable. 

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

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

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. 

Initial setup is quite straightforward. 

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

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. 

We are using Amazon SageMaker to forecast the models. We receive the data into Amazon S3 from the SAP HANA-based systems. Additionally, we are doing preprocessing and sampling for regular data.

The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code.

There are other better solutions for large data, such as Databricks.

I have been using Amazon SageMaker for three years.

The stability of Amazon SageMaker is good.

Amazon SageMaker is scalable to the project requirements.

The support from Amazon SageMaker has been positive. We create a ticket with our issues and they contact us with the solution.

I have previously used Databricks. 

The initial setup of Amazon SageMaker is straightforward. The solution is cloud-native making the process take a few minutes. Adding the extensions can take some time. We used CI/CD methods to implement the solution.

We have different teams and we had a team of two DevOps that did the implementation of the solution.

Databricks solution is less costly than Amazon SageMaker.

I rate Amazon SageMaker a seven out of ten.

We use this solution for Outlier Detection using Random Cut Forest. We intend to implement a Predictive modeling project starting in October and have not yet decided on the platform(s) we will utilize.

The challenge for us is balancing the Data Scientists, Technical vs. Analyst.

We are still learning the platform and will conduct more training as we evaluate it for other projects. The few projects we have done have been promising.

The most valuable features of this solution are the Random Cut Forest and the IDE.

I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.

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. 

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

Integration with other AWS services is seamless.

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.

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

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

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.

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

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.

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.

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.

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.