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.
Machine Learning Engineer at TechMinfy
Has hyperparameter tuning which helps to save time
Pros and Cons
- "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."
What is our primary use case?
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working with the product for six to seven months.
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Amazon SageMaker
February 2025
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What do I think about the stability of the solution?
The solution is a stable product.
What do I think about the scalability of the solution?
My company has 300 to 400 users. The solution is scalable.
How are customer service and support?
We contacted AWS support, and we are happy with them.
Which solution did I use previously and why did I switch?
We are AWS partners and blindly go with AWS products.
How was the initial setup?
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.
What other advice do I have?
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.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Disclosure: My company has a business relationship with this vendor other than being a customer:
Last updated: Jul 25, 2024
Flag as inappropriateOne-touch deployment and monitoring with customizable insights
Pros and Cons
- "One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."
- "The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
What is our primary use case?
Our primary use case is to build machine learning models and manage them using Amazon SageMaker. We use various tools provided by SageMaker, such as the studio for machine learning, pre-processing data using Wrangler, and deploying models. Some users prefer using Jupyter notebooks for their own libraries while others use features like Jumpstart or Autopilot.
What is most valuable?
One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly. Additionally, I appreciate the flexibility that the notebook provides, as it allows me to experiment with scripts.
The solution offers the SageMaker Model Monitor, which helps monitor deployed models for performance issues like bias and drift. Also, the high scalability of SageMaker allows us not to worry about the underlying infrastructure, as it automatically adjusts based on demands.
What needs improvement?
The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options.
For how long have I used the solution?
I have been working with Amazon SageMaker for about three years.
What do I think about the stability of the solution?
The stability of the solution is generally about an eight out of ten. Most instabilities arise from initial configuration errors rather than the infrastructure itself. Ensuring that the correct setup is chosen from the start minimizes these issues.
What do I think about the scalability of the solution?
Amazon SageMaker is highly scalable, rated ten out of ten. It can scale up according to the demands detected by CloudWatch, providing a seamless experience without needing to manage the underlying infrastructure.
How are customer service and support?
The customer service and support are rated as a five out of ten. The level of support depends on whether we are a premium AWS customer or not, with premium customers receiving better and more immediate support.
How would you rate customer service and support?
Neutral
How was the initial setup?
The initial setup of SageMaker can be challenging for beginners, rated as a six, but easier for those with a background in machine learning, rated as a nine out of ten. Experience with machine learning is crucial for a straightforward setup. Without it, understanding the roles of different features can be a stumbling block.
What's my experience with pricing, setup cost, and licensing?
Pricing is rated as a six, which is slightly more expensive compared to the budget yet adequate for the capabilities provided. On average, customers pay about $300,000 USD per month.
What other advice do I have?
On a scale from one to ten, where ten is the best, I rate Amazon SageMaker as a nine. For new users evaluating SageMaker, it is important to remember that it takes some learning, however, the solution is straightforward and beneficial. There are no special prerequisites except having an account.
Disclosure: My company has a business relationship with this vendor other than being a customer: consultant
Last updated: Nov 22, 2024
Flag as inappropriateBuyer's Guide
Amazon SageMaker
February 2025
Learn what your peers think about Amazon SageMaker. Get advice and tips from experienced pros sharing their opinions. Updated: February 2025.
832,138 professionals have used our research since 2012.
A tool with large online community support for model deployment, hosting, and monitoring that is expensive
Pros and Cons
- "The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
- "The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
What is our primary use case?
We use Amazon SageMaker for model deployment, hosting, and monitoring.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
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.
What do I think about the stability of the solution?
I can't comment on the solution's stability because it's still in production.
What do I think about the scalability of the solution?
The solution is easy to scale.
We have B2C Customers for the product, which would include over two million monthly recurring users.
How are customer service and support?
I have not contacted Amazon regarding the support.
The documentation and online community support have been sufficient for us so far.
How was the initial setup?
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.
What about the implementation team?
Technically, you shouldn't need anyone because it's AWS-managed services.
What was our ROI?
The solution's worth depends on the company's use cases. For us, the solution is valid because we already run on AWS.
What's my experience with pricing, setup cost, and licensing?
There is no license required for the solution since you can use it on demand.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
Cloud AWS Fellow at Bytewise Limited
Enhancing learning with intuitive model training and helpful support
Pros and Cons
- "I appreciate the ease of use in Amazon SageMaker."
- "I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."
What is our primary use case?
The primary use case of Amazon SageMaker is for training a small AI module for learning purposes. It was used for the training of a small machine learning model.
What is most valuable?
I appreciate the ease of use in Amazon SageMaker. I have not explored many features, as I am not deeply involved yet, but I aim to enhance my skills in the future.
Based on my existing experience, it was straightforward to train a small machine-learning model. I initially used AWS Skill Builder for guidance, making it manageable without encountering challenges.
In scalability, I found it highly scalable, having used the Jupyter notebook and other tools. By scaling the model, I've had a positive experience.
What needs improvement?
I would recommend having more walkthrough videos and articles beyond AWS Skill Builder. There should be additional articles within the services.
What do I think about the stability of the solution?
In terms of stability, I have not experienced any breakdowns. Although I have heard reports that it might break, I have personally never faced any issues with the stability of Amazon SageMaker.
What do I think about the scalability of the solution?
I found that Amazon SageMaker is highly scalable. I used the Jupyter notebook and explored other available tools, which were useful depending on what I utilized. I plan to enhance my model in the future, which will allow me to share more about scalability once I fully scale the models.
How are customer service and support?
The support team of Amazon is excellent. I found them to be very good.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I have not used anything else in cloud computing. Amazon SageMaker was my entry-level experience in cloud computing.
What other advice do I have?
I would give Amazon SageMaker a solid score of eight out of ten since I have not used much of its services.
Based on the small model I trained, I would recommend it to others. I have already recommended it to some colleagues, batchmates, and fellows. My advice to newcomers would be to look for walkthrough videos and articles to aid in their learning.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Nov 25, 2024
Flag as inappropriateData Scientist at a marketing services firm with 1-10 employees
Comprehensive with good machine learning platform and an easy setup and
Pros and Cons
- "SageMaker is a comprehensive platform where I can perform all machine learning activities."
- "I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."
What is our primary use case?
I am currently using SageMaker for a range of tasks including data cleaning, data visualization, exploratory data analysis, data labeling, training, and deploying models. I am also using it for machine learning projects.
How has it helped my organization?
Amazon SageMaker has accelerated our machine learning development, improved scalability, optimized resource use, and reduced costs, enabling us to deliver faster, more efficient, and scalable ML solutions.
What is most valuable?
SageMaker is a comprehensive platform where I can perform all machine learning activities. Specifically, the notebook feature is beneficial because it allows me to do everything from data cleaning to model training, dataset transformation, and data visualization on a single platform.
What needs improvement?
I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming. It would be appreciated if SageMaker made it more adaptable for different types of datasets instead of just specific ones.
What do I think about the stability of the solution?
There have been no performance or stability issues with SageMaker.
What do I think about the scalability of the solution?
SageMaker is scalable.
How are customer service and support?
I have not used customer support services. However, I did receive notifications regarding resource usage, such as a restart notice during long computations.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I have worked with generative AI, LLM models, APIs from Google, GPT, and Llama.
I switched because Amazon sagemaker comprises of most activities I needed from other different solutions.
How was the initial setup?
The initial setup was very straightforward with a left-side panel aiding in easy navigation. It was quite simple and user-friendly.
What's my experience with pricing, setup cost, and licensing?
If you are not on the free tier, SageMaker might be expensive, especially if you forget to shut down running applications. However, since I am using some free-tier services, cost efficiency is not a concern for me.
Which other solutions did I evaluate?
I have worked with other solutions such as generative AI models and APIs from other providers. I have used Jupyter notebook via Anaconda, VSCode and Google Collab for my day to day machine learning tasks.
What other advice do I have?
I would recommend SageMaker because it serves as a one-stop solution where everything needed for machine learning can be done in a single platform, without looking for other platforms to perform individual tasks.
I'd rate the solution nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Nov 11, 2024
Flag as inappropriatePartner & Chapter | Management - NodeJS - Java - C# - Python at a tech vendor with 51-200 employees
Streamlined MLOps with user-friendly cost management and scalable data handling
Pros and Cons
- "It's user-friendly for business teams as they can understand many aspects through the AWS interface."
- "Amazon might need to emphasize its capabilities in generative models more effectively."
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
What do I think about the stability of the solution?
SageMaker has proven to be stable with only one issue in our history, which is impressive.
What do I think about the scalability of the solution?
Before SageMaker, pipelines were created manually. Since using SageMaker, I've been impressed with its high scalability, rating it a ten out of ten.
How are customer service and support?
We have used free support tickets, and the response time is normal, usually in one or two days.
How would you rate customer service and support?
Positive
How was the initial setup?
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.
What about the implementation team?
We managed the implementation with two people focusing on understanding and making additional tests.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
We are migrating some models to Azure to evaluate its GPT enterprise version.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Disclosure: My company has a business relationship with this vendor other than being a customer: consultant
Last updated: Oct 29, 2024
Flag as inappropriateDEMI Instructor at a non-tech company with 501-1,000 employees
Streamlined machine learning workflow with room for enhanced user interface
Pros and Cons
- "SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
- "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."
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working with SageMaker for five or six months.
What do I think about the stability of the solution?
I haven't faced any performance or stability issues with SageMaker.
What do I think about the scalability of the solution?
Adding additional resources or scaling up to meet demand is not as straightforward as I would like.
How are customer service and support?
I have not had any experience interacting with customer support or technical support for SageMaker.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
I can't remember the initial setup process clearly after three months. However, there was a learning curve involved when I began using SageMaker.
What about the implementation team?
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.
What's my experience with pricing, setup cost, and licensing?
The pricing is based on usage, and I find it reasonable for what we use it for.
What other advice do I have?
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.
Which deployment model are you using for this solution?
On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 31, 2024
Flag as inappropriateLead Technical Product Owner - AI & ML at a transportation company with 10,001+ employees
Stable solution that's worth the money but lacks reporting services
Pros and Cons
- "We've had no problems with SageMaker's stability."
- "SageMaker would be improved with the addition of reporting services."
What is our primary use case?
I mainly use SageMaker for deploying, using, and running our models.
What needs improvement?
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.
For how long have I used the solution?
I've been using SageMaker for six to eight months.
What do I think about the stability of the solution?
We've had no problems with SageMaker's stability.
How are customer service and support?
We are premium partners with AWS, so we have complete 24/7 support from them.
How was the initial setup?
The initial setup was very easy and quick, though deploying using cloud formation templates was difficult for us.
What about the implementation team?
We used an in-house team.
What's my experience with pricing, setup cost, and licensing?
SageMaker is worth the money for our use case.
What other advice do I have?
I would give SageMaker a rating of six out of ten.
Which deployment model are you using for this solution?
Public Cloud
Disclosure: My company has a business relationship with this vendor other than being a customer: partner
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Updated: February 2025
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Thank you, Arhum, for such a well-written and insightful article! Your clear explanations and practical examples made the topic so much easier to understand. This has been incredibly helpful, and I’m excited to apply these insights to my own projects. Looking forward to reading more from you.🙌🙌