Amazon SageMaker Reviews

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 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.

Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker.

Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker.

I have been using Amazon SageMaker for six months.

Amazon SageMaker is a stable solution.

Amazon SageMaker is a scalable solution.

I have previously worked with Google Cloud Platform (GCP). We first moved from GCP to Databricks because it was cost-efficient. Now, we are moving from Databricks to Amazon SageMaker to see whether it serves our purpose and is beneficial with respect to cost and time.

The solution’s initial setup was straightforward. On a scale from one to ten, where one is difficult, and ten is easy, I rate Amazon SageMaker a six out of ten for the ease of its initial setup.

The solution's initial deployment was a one-week job, but the final pipeline run was 12 to 15 hours.

Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker, but its storage is cheap.

On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten.

I am doing a benchmarking study between Databricks and Amazon SageMaker to determine the most cost-efficient and effective for our organization.

Amazon SageMaker is a pretty good solution for users who don't have any knowledge about their data and want to try different scenarios. The solution is fast and uses less code.

Overall, I rate Amazon SageMaker a seven out of ten.

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.

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 use Amazon SageMaker primarily for designing and performing experiments with recommendation systems. It's mainly about classification, as most recommendation systems are based on classification.

It is crucial for the design and execution of multiple experiments concurrently, allowing me to concentrate on model design. I can use SageMaker for hyperparameter optimization and eventually for deploying models to production.

The most valuable features are the ability to store artifacts and gather reports and measures from experiments. Additionally, the integration with AWS infrastructure and the capability to create a hybrid grid infrastructure with scaling are important.

The model repository is a concern as models are stored on a bucket and there's an issue with versioning. Also, being unable to create routing other than based on TCP/IP protocols and HTTP poses limitations.

I have been working with Amazon SageMaker for close to three years.

I find the performance of SageMaker stable, especially when I use it with a Kubernetes cluster. There have been no significant issues noted.

It is scalable, particularly with the AWS infrastructure, including clusters and scheduling solutions like Airflow.

I rarely use customer support since the platform is stable. Any issues, though rare, seem to be resolved well.

Positive

I have worked with other AI development platforms, such as Kubeflow and MLflow, as separate platforms.

The initial setup is straightforward if you have basic knowledge related to AWS Cloud and its infrastructure.

The pricing is reasonable from my point of view, but it depends on factors like project size, potential income, data size, and user number.

I have experience with other AI development platforms, including Kubeflow and MLflow.

I suggest properly sizing your project to manage potential costs effectively. If the project is small, starting with something simpler and less expensive may be better.

I'd rate the solution nine out of ten.

I use the solution since it is good. I have no issues with the solution as it suits my needs. Amazon SageMaker was used in our company to train an ML model. One of the trainers in our organization used Amazon SageMaker to train an ML model. I haven't had the opportunity to use products other than Amazon SageMaker. I am satisfied with Amazon SageMaker.

I feel that the area around the interface in AWS is overall confusing. The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product. The tool is not simplified enough for beginners to use. From an improvement perspective, the tool needs to be simplified enough for beginners to use.

I have used Amazon SageMaker once or twice in the last six months. My company operates as a system integrator for Amazon.

Stability-wise, I rate the solution an eight to eight and a half out of ten.

It is a scalable solution. Scalability-wise, I rate the solution an eight out of ten.

The number of uses of Amazon SageMaker varies from project to project. For most of the projects, the employees in our company depend on Azure platforms. Based on requests from our company's clients, we use Amazon SageMaker. Presently, five or six teams in our company use Amazon SageMaker.

I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten.

Positive

The product's initial setup phase and configuration were easy.

The product's installation phase requires two people.

The solution can be deployed in a few hours.

I and one of the trainers in our company were involved in the installation process of the product.

As the product does the job for what is required in our organization, I feel that it saves time.

Amazon SageMaker is a very expensive product. There is a need to make monthly payments towards the licensing cost attached to the solution. Even though I had initially used Amazon SageMaker's free trial version, Amazon charged me 130 USD for two to three days of usage. There are no extra charges to be paid apart from the resources that users use.

I have not integrated Amazon SageMaker with other products in our company.

If someone plans to use the free trial version of Amazon SageMaker, then the person should be aware that it is chargeable since Amazon has not mentioned it in a written format. For enterprise-level users, there is nothing to worry about since their organization will take care of the costs attached to the solution.

I rate the overall tool 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.