Amazon SageMaker Reviews

My company uses Amazon SageMaker since we are into data analytics involved in predictions and focusing on various model executions, working with some top companies. Most of the use cases of the solution for my company stem from the fact that we need to understand various customer chain models, including customer retention or customer acquisition models, to leverage more revenue. Sometimes, the solution functions in batch mode or real-time mode. In case a customer contacts an IVR agent or the customer support team for help, we do modeling in real-time and deliver to Amazon SageMaker endpoint, ensuring how the robotics part responds to the queries of the customer.

The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides. It is important to note that since the ensemble model has limitations, it takes more time to process.

The most valuable feature of the solution is Amazon SageMaker Canvas. The training and algorithm-based XGBoost modeling make it a good product for a startup, especially for companies that want to explore something but don't have a proper model. The instrument will be helpful for those who want to explore something.

Amazon SageMaker should concentrate and get the performance of the ensemble model to be good enough for its users.

Improvements are needed in terms of performance for not all but some of the models, especially whenever we use the product for image classification or something. In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user.

I have been using Amazon SageMaker for four to five years. My company is a customer of AWS, and we have an advanced technology partnership with Amazon.

Stability-wise, I rate the solution a nine out of ten.

Scalability-wise, I rate the solution an eight out of ten.

Around 20 to 25 people in my company use Amazon SageMaker.

The technical support for the solution is good, but it is a paid service. The technical support for troubleshooting issues is chargeable, so ten percent of AWS billing will be the cost for technical support. I rate the solution's technical support an eight out of ten.

Positive

Along with Amazon SageMaker, I use other services from AWS, like AWS Glue, Athena, Redshift, SQS, SNS, and Airflow.

On a scale of one to ten, where one is a difficult setup, and ten is an easy setup, I rate the setup phase a nine.

The solution is deployed on the cloud.

The deployment phase takes around 15 to 20 minutes since the product has good integration capabilities with other platforms like Jenkins and Terraform.

Our company uses Jenkins pipeline and Bitbucket for the deployment process. Everything is moved from CodeCommit to Bitbucket, after which the Jenkins pipeline takes it from Bitbucket and deploys it to SageMaker. We can do the deployment in the cloud as well, but we do it with Bitbucket and Jenkins since they allow for good integration with Amazon SageMaker, which is also easy for us to make it move.

We have a team consisting of solution and database architects in which, most of them are AWS-certified individuals capable of carrying out troubleshooting procedures in case of issues who take care of the solution's deployment process in our company.

I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees.

From an exploration perspective, for people who cannot afford hardware at the physical location, it would be good to use the services from a cloud for leverage. It is easy to scale up or down when operating on an AWS Cloud. Suppose we have an on-premises or hybrid solution. In that case, we need to look at the economic structure of the organization, after which bringing everything into a physical location can get really complex. I suggest others explore using AWS before deciding on future plans.

I rate the overall solution a nine out of ten.

I use the solution to build machine learning models and deploy them on endpoints.

The cost of managing our organization’s infrastructure is just too much. It’s easier to use AWS.

The product aggregates everything we need to build and deploy machine learning models in one place. We log in to the cloud and have everything we need to build and deploy models.

The product must improve its documentation. The documentation must be made clearer and more user-friendly. Sometimes, we run into issues with setup. However, it's not that often.

I have been using the solution for about two years.

The stability is solid. I've been using the tool for a while. I haven't had major issues with it.

We have 600 people in our organization. More than 300 people are developers. Everybody uses AWS. The tool is very scalable. I rate the scalability a nine out of ten.

I had an issue logging into my AWS account. So I contacted the support persons. They were helpful.

Positive

The solution is deployed on the cloud. We do not manually install the solution on our machine. We do have a CLI on our machine.

The solution is relatively cheaper. Azure might be a little cheaper than AWS. However, AWS has all the services we need in one place.

The solution works. It’s better than most of the other options available. We go through a long process to get the model in the hands of the users. SageMaker caters to all the processes involved with pre-built services. It makes the whole process very easy. Sometimes, the cost can be an issue, but it has all the right services we need. It is very smooth. Overall, I rate the solution a seven 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.

I mainly use SageMaker for deploying, using, and running our models.

SageMaker would be improved with the addition of reporting services. In addition, the models available in SageMaker are not enough for most of our use cases and require customization to be useful.

I've been using SageMaker for six to eight months.

We've had no problems with SageMaker's stability.

We are premium partners with AWS, so we have complete 24/7 support from them.

The initial setup was very easy and quick, though deploying using cloud formation templates was difficult for us.

We used an in-house team.

SageMaker is worth the money for our use case.

I would give SageMaker a rating of six out of ten.

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.