Amazon SageMaker Reviews

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.

I am currently working with AWS services like SageMaker, S3, EC2, VPC, load balancing, auto scaling, and RDS. SageMaker is used primarily for AI projects to build, train, and deploy AI models from scratch.

SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project. It provides lifecycle configurations, similar to EC2's user data, for running scripts on instances. It also offers VPC features to isolate SageMaker instances when needed, which is a valuable use case.

The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement.

I have been working with SageMaker for about one week due to a project related to AI.

I have not encountered any stability issues with SageMaker so far.

Scaling SageMaker has not been an issue. If required, I can increase the instance size to a higher tier as needed.

I haven't needed to contact AWS technical support for this project.

Positive

When I first started working with SageMaker, I was unfamiliar with it. I consulted AWS articles and searched online to understand how SageMaker works.

I was responsible for deploying SageMaker for my client and configuring notebook instances with VPC and subnets.

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

I recommend SageMaker for ML projects if you need to build models from scratch. If you do not want to maintain the instances, consider using Datablock instances. On a scale of one to ten, I would rate SageMaker a nine.

We use Amazon SageMaker for model deployment, hosting, and monitoring.

There is a lot of control in the solution over which terms you want to pick and choose. You don't have to pick the end-to-end machine learning operation solution. You can just choose deployment or training if you want, a benefit I saw in the solution.

You can leverage AWS's accelerated hardware to run your machine learning models, which is beneficial for improving a model's performance in terms of runtime, which is how long it takes to execute. Amazon SageMaker is an AWS product, and the company I work for already hosts all its services on AWS. Everything works well together if you're already on AWS.

Amazon SageMaker is expensive. It is more expensive than Databricks because it has a pay-as-you-go model with SageMaker.

The pricing of the solution is an issue.

In SageMaker, the monitoring does not support data captured in a protocol called protobuf since it only accepts JSON and CSV. We use protobuf to exchange messages. We have to write our own logic to serialize protobuf, which is more work, and it'd be better if SageMaker provided out-of-the-box support for that.

In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV. Amazon SageMaker can start with Protobuf since it is a popular messaging protocol.

I have been using Amazon SageMaker for six months. We are using the latest version of the solution. My company has a partnership with Amazon.

I can't comment on the solution's stability because it's still in production.

The solution is easy to scale.

We have B2C Customers for the product, which would include over two million monthly recurring users.

I have not contacted Amazon regarding the support.

The documentation and online community support have been sufficient for us so far.

There is nothing to install since it is all cloud-based.

Deployments are a matter of minutes, depending on what kind of tool you use. I use AWS CDK for deployment, and with CDK, if you know what you're doing, it takes 15 minutes.

The solution is deployed on a public cloud.

The number of people required for deployment depends on the company and how fast they want to get it done. I had once completed PoC and the implementation part, and it took me some time, but deploying it alone is not an end-to-end solution. The deployment process requires the expertise of data scientists to build the model and an engineer to deploy it. Deployment would involve a maximum of two individuals.

Technically, you shouldn't need anyone because it's AWS-managed services.

The solution's worth depends on the company's use cases. For us, the solution is valid because we already run on AWS.

There is no license required for the solution since you can use it on demand.

Amazon SageMaker is definitely not the best product out there. I recommend that one can quickly do prototyping on SageMaker. It is easy to take your workload to the AWS Cloud. Amazon SageMaker's setup is very fast, so you'll be able to validate all your hypotheses pretty fast.

Overall, I rate the solution a seven out of ten.

In my projects, I work with Amazon SageMaker to do face recognition using Amazon SageMaker and Amazon Lex. The platform is used for integrated development and utilizes features like Amazon Cognition for analyzing images and videos, as well as comparing images.

Amazon SageMaker provides a structured workflow from data sourcing, data processing, and data labeling to model training and deployment. This end-to-end workflow simplifies tasks and avoids confusion with other platforms, enhancing our efficiency. Moreover, its performance is commendable, especially in terms of computing speed, saving time effectively.

SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure. These features simplify the end-to-end machine learning workflow, including data labeling, preparation, reprocessing, training, deployment, and monitoring.

The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services. 

Additionally, the platform struggles with tuning large models, which is a significant limitation.

I have been working with SageMaker for five or six months.

I haven't faced any performance or stability issues with SageMaker.

Adding additional resources or scaling up to meet demand is not as straightforward as I would like.

I have not had any experience interacting with customer support or technical support for SageMaker.

Positive

I have not used other AI development platforms like Azure or GCP in my current work. My use of SageMaker is based primarily on its integration into our existing workflows.

I can't remember the initial setup process clearly after three months. However, there was a learning curve involved when I began using SageMaker.

I am unsure about the size of the implementation team specific to the use of SageMaker, however, it was used by our team of ten people.

The pricing is based on usage, and I find it reasonable for what we use it for.

I suggest SageMaker for anyone looking to facilitate an end-to-end machine learning workflow, from preparing data to deploying models, due to its comprehensive feature set.

I'd rate the solution eight 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.

I use SageMaker to use a "bring-your-own-model" setup. For SageMaker AutoML, we're fine and happy with it. It is restricted because you can't move through multiple algorithms. It seems to work only with two. One of the things I am doing is prototyping, and it's proving quite difficult to get our model working how we want it to. It's proving complex with many moving parts, and the documentation is only partially helpful. SageMaker requires a lot of work to get it working.

I spent the last four months trying to get a prototype working and exploring to bring in a model while exploring alternate models and making prototypes work. We've stepped back to AutoML for now. We might be using EKS, so we bring our containers. Within the containers, we can work with what we need to work with.

The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework. When you couple it with step functions, you can do some very powerful things. It manages deployment, you have model monitoring, and you have model quality checks. It's got a lot of end-to-end services one needs to get a full machine-learning pipeline running. While I say that I had a struggle and blame the product partially, I am also impressed with the ecosystem. I would still use it over and above other competing products, but I don't know the Google setup. I have worked very briefly with Azure, so I can't do a proper card-to-card comparison, but I do like the ecosystems AWS brings. If a client came along and asked me to set up a machine learning ecosystem, a full machine learning production deployment, I would use Sagemaker.

The solution is complex to use.

Some additional functionality would be for them to provide sample end-to-end card formation templates and try to unify the setup. At the moment, as you move from one set of documentation to the next, some of the documentation is for bringing your model, some of the documentation is for SDK, some of it's for API, some of it's for command lines, and some of it's for step functions. None of the documentation seems to be end-to-end. There are gaps in the documentation. It proves to require a lot of digging from the user to figure it out. I did get through the AWS machine learning specialty certification, but that proved to be a bit superficial. Though it covered a lot of ground, it didn't have the detail one would need for Sagemaker. I am self-taught in a lot of the stuff. I could dive deeper into some code and take time to get examples running. But I was consulting a startup, and they needed to move quickly.

I was hoping SageMaker would be easier to work with because I was expecting there would be examples we could repurpose that were more complete.

The new functionality I'd like to see is Amazon tuning attention to the documentation sets and the templates.

I've been using Amazon SageMaker for about two and a half years.

Stability is not a relevant metric anymore because SageMaker runs on its own underlying AWS serverless infrastructure, which is 100% reliable.

Folks better than me with more extensive resources and time have run and checked SageMaker ten times a second every second for 1,000 hours to see if they got a drop, but they haven't. It's serverless and bulletproof.

I rate the scalability a ten out of ten. Part of the problem is that AWS has limited the functionality of SageMaker in many ways to make it scalable. So it's scalability first and then functionality second.

The solution works well for medium-sized businesses and up. But even for small businesses, you can do some simple and quick elastic endpoints and get going quickly. The problem is the amount of work it takes for people to know what they're doing with Sagemaker, and those people are probably rare. I've been able to get things up and running in most cases in all sorts of AWS services, but I'm struggling. Small, medium, and large enterprises could use SageMaker with an automatic model, but it depends on the people's skills doing the deployment. A small business probably couldn't afford contractors, consultants, or data scientists. It's not about AWS. It's a problem with classic data science skills.

I rate the initial setup a three or two out of ten because it's very complex.

You don't pay for Sagemaker. You only pay for the compute instances in your storage. SageMaker is free.

I've had a little bit of a look at Azure, but I didn't get into the level of detail I did with Sagemaker. I have worked reasonably intentionally with DataRobot and H20. But SageMaker is a way bigger, way more capable platform. The AutoML is very simple, and it is much, much cheaper. The cost of SageMaker is nothing. By contrast, if you're using DataRobot, you'll pay $100,000 plus for a five-year license.

Anyone doing on-prem at the moment for anything but their core datasets or legacy systems that can't be moved is just paying useless money.

I rate Amazon SageMaker a seven out of ten. I'd recommend it to other users. It's worth syncing the time and effort into getting it running.

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.