Amazon SageMaker Reviews

I use it for modeling large amounts of production data. We don't have the time and it's a large amount of production data. So, it's not physically possible to eliminate or find the co-relations, run it through, basically setting and coding in Python. So it's much easier. 

You just have your drag and drop. So if you have the Python knowledge for that, it's very good. We basically suggest that these people to use it as well. 

The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate.

The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful. 

Additionally, the user manuals can be difficult to navigate without prior knowledge. We often test new features for clients in small groups, and I've heard feedback that the documentation could be more user-friendly.

I have been using it for around nine months. 

I would rate the stability an eight out of ten. They could add features, which would be nice.

Scalability is a great point for AWS. But then again, when it comes to manufacturing, it's about people in the plant. Sometimes, they don't use the product at all. 

Even though it's popular and used by many companies, people tend to stick with other solutions. However, since Arain assumed your data center should be in-country, most people are now welcoming these cloud solutions.

The suitability of this solution's usage depends on the use case and the company size. If it involves a lot of variables and is difficult to manage manually, the tool is perfect.

We get support from the Dubai guys. They come for training and provide any technical assistance needed. It's nice to have in-house support, so I'd rate them a nine out of ten.

Our Infotech company comes under shared services. So, technically, we provide solutions for every other department.

Plus, we are working with AWS. Plus, we have, we have in-house tools that we develop. We also use Microsoft tools like Teams, Office, and SharePoint, as well as EDMS.

We also use SageMaker and Databricks.

The initial setup was very simple for me. But for other people, so we set up the environment for people in here. So, technically, I don't think process people would have faced much challenge about that because we usually set it up, call them, and we share a screen, and we set it up for them. So it's all there. 

Initially, when someone has no knowledge about that, it would be hard. If you don't know the AWS essentials, knowing the correct kind of storage might be a challenge.

Initially, deployment will take around maybe an hour. But after that, you just get used to it, so it is pretty much easy. Like, about 15 minutes, you're done, you're explaining. 

For us, it's always on on-premise things, even better be a data lake or data warehouse or modeling or anything. So, going from, like, hardcore coding every line in bit fit and embedding it on your own. Having a feature like that is just a relief. So it took a lot of time because one of the popular manufacturing companies underwent a hack. And after that, most of the manufacturing companies don't promote cloud solutions.

Our data LAKE is on-prem. So, basically, we just move the modules that are required at that point in time and pull it out.

There is room for improvement in the pricing. The pricing could be better, especially for querying. The per-query model feels expensive. It would be better to have tiered pricing based on query sets or usage. Some services definitely need pricing adjustments.

We tried Azure, and their tech support wasn't great. It took a long time for them to get back, and they might not have much regional coverage. I don't know if they have it, but AWS dominates the region, and most companies use it. When we were looking for solutions, we did some research, but the feedback for Azure wasn't positive.

Overall, I would rate the solution a nine 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.

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.

We use Amazon SageMaker for a specific project involving an airline company to analyze historical flight data. Initially, we created a proof of concept locally and then developed a data pipeline to enhance the flow for large data models. This project benefited from SageMaker's ability to handle large data with TensorFlow, leading to a more efficient MLOps process.

SageMaker has streamlined the process for us, particularly in handling TensorFlow and making data management auto-scalable. This has allowed for a more efficient process, especially when discussing with DevOps about MLOps capabilities.

The most important feature is the ease of controlling costs with pricing calculators, which is crucial due to our limited budget. The low-code MLOps aspect simplifies data science work and makes it accessible for engineers without a DevOps background. It's user-friendly for business teams as they can understand many aspects through the AWS interface.

While not specific to SageMaker, I've observed a trend where companies are more attracted to the marketing of Gemini and GPT, leading some to migrate their pipelines to these platforms. Amazon might need to emphasize its capabilities in generative models more effectively.

SageMaker has proven to be stable with only one issue in our history, which is impressive.

Before SageMaker, pipelines were created manually. Since using SageMaker, I've been impressed with its high scalability, rating it a ten out of ten.

We have used free support tickets, and the response time is normal, usually in one or two days.

Positive

The initial setup was not complex, taking us about one month to fully understand and set it up, with the actual setup time being around one to two weeks.

We managed the implementation with two people focusing on understanding and making additional tests.

Compared to other top cloud services like Azure and Google Cloud in Brazil, AWS pricing is competitive and merits a rating of five out of ten.

We are migrating some models to Azure to evaluate its GPT enterprise version.

It's important for new users to clearly define the testing process and understand what they aim to achieve. SageMaker facilitates a fast, secure, and scalable MLOps process.

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 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.

Our primary use case for Amazon SageMaker involves saving infrastructure requirements by deploying machine learning or AI models. Instead of preparing new servers, which incurs a high cost, we use SageMaker to develop models temporarily. We use SageMaker services only for the short duration needed to create AI models and then terminate them.

Amazon SageMaker has helped our organization manage infrastructure costs by avoiding the need to set up new, costly servers for our AI model development. It provides a ready-made platform that allows us to quickly start our machine learning projects and is beneficial for short-term projects.

The most valuable feature of Amazon SageMaker is SageMaker Studio. It is a web-based, integrated model that allows numerous connections and includes all steps needed for machine learning development. The visual steps provided in the Studio are highly beneficial. Additionally, the advanced monitoring feature helps us keep track of infrastructure usage.

While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker. Enhancing encryption and overall security could uplift the platform and build more trust among users. Another area of improvement is the cost, which can become quite high during prolonged use.

We have been working with Amazon SageMaker for about one year.

Amazon SageMaker is quite stable. We have never had to reinstall or reconfigure it, indicating that whatever we set up initially worked well. However, the stability score is eight because users occasionally run into issues.

The support from Amazon SageMaker is very good. The chat support is well-maintained, and support team members possess in-depth knowledge about SageMaker, providing immediate help. However, the wait times for support can occasionally delay assistance.

Positive

The initial setup of Amazon SageMaker is quite easy. The online help from AWS is comprehensive, and their support team is knowledgeable. Ample documentation and expert chat support make the setup process smooth, although some might run into issues during the setup.

Amazon SageMaker provides a ready-made tool and platform that allows quick initiation of AI models, which is of interest to many. However, the long-term cost can be high if used continuously, which can affect its adoption for regular, ongoing use.

The license cost for Amazon SageMaker ranges between seven thousand to fifteen thousand dollars per month depending on various factors such as the model, amount of data, and geographical locations involved.

Amazon SageMaker is a fantastic tool for short-term use. The billing increases significantly for prolonged use, but for short-term projects, it is quite economical. For those considering it, it is highly recommended that they use it for quick, temporary projects and then move out.

I'd rate the solution eight out of ten.

We use Amazon SageMaker primarily for training and deploying end-to-end models for our specific use cases. We take models from the interface and deploy them to the staging environment, ensuring they are monitored 24/7. This tool is essential for deploying models. 

The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time. 

One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful.

I have been working with the product for six to seven months. 

The solution is a stable product.

My company has 300 to 400 users. The solution is scalable. 

We contacted AWS support, and we are happy with them. 

We are AWS partners and blindly go with AWS products. 

Regarding the initial installation, setup, and deployment, I would rate it as medium difficulty. Since it operates within the AWS ecosystem, you must follow specific rules and understand how AWS works. It can take around four to five months to fully deploy a model, understand its running and training processes, and get everything set up properly.

If you want to use Amazon SageMaker for the first time, I would advise completing one of the AWS certifications and reading the documentation thoroughly. Having someone experienced with the product to guide you can also be very helpful.

Despite its high price, the tool is continually evolving, and updates are frequent and relevant. However, due to its pricing and some issues, I would rate it a seven 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.

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.