Amazon SageMaker Reviews

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.

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.

The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options.

I have been working with Amazon SageMaker for about three years.

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.

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.

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.

Neutral

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.

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.

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.

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.

Amazon SageMaker is a collaborative tool for our data science projects. It allows us to integrate efficiently, write and review code, and access all the necessary project tools.

Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modeling and algorithms.

Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS.

I have been using Amazon SageMaker for the past two years.

I've never encountered issues with SageMaker's scalability. AWS provides all the necessary resources in terms of power and capacity.

The initial setup is straightforward. We have a team from the infrastructure department that ensures the system runs smoothly. The data science team also plays a role in monitoring the effectiveness of the models. The deployment process usually takes two to three months for the whole project, with various strategies involved. SageMaker integrates well with AWS features, and when deploying, I typically set up APIs to make the model accessible to other systems and connect it with GitLab for easier model control.

In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions.

I would rate Amazon SageMaker a nine out of ten because while it has all the necessary features, there could be improvements in making the data flow more manageable.

I use Amazon SageMaker to develop modules with data stored in AWS, extracted from SAP. After building the modules, I deploy them to assess their performance and efficiency.

Amazon SageMaker has significantly enhanced our organization by consistently introducing new features like model tracking and recently integrating with MLflow. This integration provides me with increased flexibility for experimentation, making it easier to explore and implement innovative solutions.

The most beneficial feature for streamlining my machine learning workflows in Amazon SageMaker is MLflow. It allows me to experiment more effectively before finalizing decisions which enhances the progress of my machine learning projects.

Amazon SageMaker's integration with Jupyter Notebooks has significantly improved my data exploration and experimentation process. The built-in IDE is excellent and has been useful from the beginning, providing a seamless and effective platform for my work.

The most valuable feature of Amazon SageMaker for me is the model deployment service. Serving the model is crucial because it seamlessly scales with the operation of the model, providing efficient infrastructure that adapts to the scaling needs, and ensuring optimal performance.

Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process. Having integrated intelligence to suggest hyperparameters would be beneficial for optimization.

I have been working with Amazon SageMaker for two years.

I would rate the stability as a six out of ten since there is room for improvement in the user experience to enhance both scalability and stability.

I would rate the scalability of Amazon SageMaker as a seven out of ten. We have about ten users of it at our company.

The tech support for Amazon SageMaker is not good, especially for new users. There is a need to scale and improve support services to provide better assistance for users, particularly those who are less experienced with the platform. I would rate the support as a five out of ten.

Neutral

I would rate the easiness of the initial setup as a six out of ten. The process of using Amazon SageMaker has some challenges, mainly due to the complexity of multiple components. Streamlining the deployment process with better scripting support would be beneficial, addressing the difficulties associated with managing various moving parts in the platform.

The deployment process in Amazon SageMaker is smooth once the initial setup is done. Integrating with other AWS services like RDS is a key aspect, requiring attention to connections and overall integration for a successful deployment.

I would rate the costliness of the solution as a six out of ten. It could be a bit cheaper.

I evaluated other options like ML Studio and a few others but chose Amazon SageMaker because of my familiarity with their services and features.

I use Amazon SageMaker in our production environment for making predictions in batches, ensuring efficient and scalable processing of large datasets.

Overall, I would rate Amazon SageMaker as a six out of ten.

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.

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.

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.

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.

There have been no performance or stability issues with SageMaker.

SageMaker is scalable.

I have not used customer support services. However, I did receive notifications regarding resource usage, such as a restart notice during long computations.

Positive

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. 

The initial setup was very straightforward with a left-side panel aiding in easy navigation. It was quite simple and user-friendly.

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.

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.

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.

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.