Amazon SageMaker Reviews

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 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.

I know about SageMaker and its capabilities, and what it can do, but I have not had any hands-on experience.

It's a machine learning platform for developers to create models.

There are pre-built solutions for everything. For example, if you want to build a deep learning model, we already have AlexNet, the internet, and all of the packages are inside. You don't have to recreate the same thing from scratch, but instead, you can use their models. You can use their model and use their data, then you can use your data.

I am a big fan of their computational storage capabilities. It's a relational database itself. It's a new SQL and you get different types of services. That is one of the best things that I like when doing my research.

I cannot quantify it as it is based on your requirements, but I can say that it's very flexible and you are able to increase all of the RAM and the GPU support.

They are doing a very good job on their end. They are evolving. I have learned that they have already integrated an IDE into Amazon SageMaker. They are doing a good job of evolving.

The pricing is complicated and should be simplified.

I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox. This would be beneficial for newcomers, especially those who are getting into the cloud space. They could explore this area and get all of the aspects including data engineering, data recognition, and data transformation.

I have been familiar with this solution for three months.

From my findings, it's quite stable.

Amazon promises that they will provide you with stability, and it is quite a stable platform.

If you are facing any issues it may be related to the computational storage capability that you opted for. For example, if you are opting for a full code row and you have a lot of data that is taking a lot of time, then you have to go back to retrieve it. That flexibility is within the AWS, but you have to bear the cost.

It's quite scalable.

The technical support is very good and I am satisfied with it.

I researched Amazon SageMaker on my own.

The initial setup is straightforward. It's not complex.

The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation. It is already decided, but if you want to have a look at how it is broken down or how they are calculating it, then they provide a tool where you can go and specify your options. These include what you want, how much storage, the RAM, and whether you want GPU support. You can include everything and then you can get the estimated cost.

AWS is an additional cost.

We are not with Anaconda Solutions, we use their packages. We are exploring their interface and it's capabilities. We are currently on a different tool, on a different platform. We are using their package managers to access the set of solutions deployed.

I am not exposed to Amazon SageMaker but I know it's capabilities. I know exactly what we can do and how we can do it. We have been provided with several solutions for image processing, speech processing, and text processing. They have provided a built-in solution for every task. You can use tools for deploying your model, you just have to plug and play.

There is no cessation from what I can see. Whatever they have in the industry, they can solve 98% of the use cases.

There is also data engineering which is quite important. It's where the real work is done.

Amazon has already provided a free slot for each of the services that we have done. With Amazon SageMaker, however, I have not seen that.

I have not yet explored everything, but they are doing good work.

In terms of the dashboard, I can say that I have not explored the visualization aspect very much, but they have their tools. I don't know how flexible it is and how much customization you can do. That's something on the visualization side that I don't enjoy very much. My interests are mostly towards data engineering or data science.

I would rate this solution a nine 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.

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. 

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.