Amazon SageMaker Reviews

The primary use case for Amazon SageMaker is leveraging its compute power, particularly for tasks like securing LMM notebooks using node instances. Additionally, its GPU capabilities are valuable for executing large language models. Users can create endpoints and access them from anywhere as needed.

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed.

The main function I prefer in Amazon SageMaker is the ability to create endpoints for large models. I haven't explored features like Studio Lab yet, but I've found the tutorials very helpful. The platform is user-friendly, with documentation attached to everything, making it easy to navigate and learn. Overall, I especially like the Studio Lab feature.

In the Studio Lab, tutorials provide direct snippets for tasks like connecting to S3 from Amazon SageMaker. These standard snippets make implementation straightforward and simplify the development process for me.

In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints. 

In the three months I've been using it, I've noticed that higher GPU instances can be quite costly. To mitigate this cost impact, serverless GPUs would be beneficial.

I have been working with the product for three months. 

I rate the solution's stability a nine out of ten. 

I rate the tool's scalability an eight out of ten. No issues with scalability as long as we ensure we have the necessary quotas in place before implementing a scalable process. I needed to request quota increases for certain services beforehand, and once those were provided, I could adjust the main and max nodes accordingly based on our planned requirements. My company has 25 users. 

We can schedule a direct call with the support team. 

Amazon SageMaker's Studio Lab feature differentiates it from products like Azure ML Studio. With Studio Lab, I can directly interact with the environment, making navigating and accessing documentation easier. In contrast, finding documentation and navigating Azure ML Studio was challenging.

However, we also use Azure for the Azure OpenEdge service, which operates on a pay-per-minute token basis. This payment model is not available in Amazon SageMaker. 

The initial setup and deployment process for Amazon SageMaker is straightforward. The only complexity I encountered was gaining access to the needed resources, which relied on coordination with the DevOps team. Once I had access sorted out, implementing my ideas for large language models and other models was comfortable. 

The tool's pricing is reasonable. 

I rate the overall solution an eight out of ten. 

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 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.

Positive

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. 

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 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.

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.

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.

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.

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.

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

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

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

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

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

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

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

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

Positive

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

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

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

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

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

I'd rate the solution nine out of ten.