Amazon SageMaker Reviews

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.

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.

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.

I would recommend having more walkthrough videos and articles beyond AWS Skill Builder. There should be additional articles within the services.

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.

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.

The support team of Amazon is excellent. I found them to be very good.

Positive

I have not used anything else in cloud computing. Amazon SageMaker was my entry-level experience in cloud computing.

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.

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 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.

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.

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 know about SageMaker and its capabilities, and what it can do, but I have not had any hands-on experience.

It's a machine learning platform for developers to create models.

There are pre-built solutions for everything. For example, if you want to build a deep learning model, we already have AlexNet, the internet, and all of the packages are inside. You don't have to recreate the same thing from scratch, but instead, you can use their models. You can use their model and use their data, then you can use your data.

I am a big fan of their computational storage capabilities. It's a relational database itself. It's a new SQL and you get different types of services. That is one of the best things that I like when doing my research.

I cannot quantify it as it is based on your requirements, but I can say that it's very flexible and you are able to increase all of the RAM and the GPU support.

They are doing a very good job on their end. They are evolving. I have learned that they have already integrated an IDE into Amazon SageMaker. They are doing a good job of evolving.

The pricing is complicated and should be simplified.

I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox. This would be beneficial for newcomers, especially those who are getting into the cloud space. They could explore this area and get all of the aspects including data engineering, data recognition, and data transformation.

I have been familiar with this solution for three months.

From my findings, it's quite stable.

Amazon promises that they will provide you with stability, and it is quite a stable platform.

If you are facing any issues it may be related to the computational storage capability that you opted for. For example, if you are opting for a full code row and you have a lot of data that is taking a lot of time, then you have to go back to retrieve it. That flexibility is within the AWS, but you have to bear the cost.

It's quite scalable.

The technical support is very good and I am satisfied with it.

I researched Amazon SageMaker on my own.

The initial setup is straightforward. It's not complex.

The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation. It is already decided, but if you want to have a look at how it is broken down or how they are calculating it, then they provide a tool where you can go and specify your options. These include what you want, how much storage, the RAM, and whether you want GPU support. You can include everything and then you can get the estimated cost.

AWS is an additional cost.

We are not with Anaconda Solutions, we use their packages. We are exploring their interface and it's capabilities. We are currently on a different tool, on a different platform. We are using their package managers to access the set of solutions deployed.

I am not exposed to Amazon SageMaker but I know it's capabilities. I know exactly what we can do and how we can do it. We have been provided with several solutions for image processing, speech processing, and text processing. They have provided a built-in solution for every task. You can use tools for deploying your model, you just have to plug and play.

There is no cessation from what I can see. Whatever they have in the industry, they can solve 98% of the use cases.

There is also data engineering which is quite important. It's where the real work is done.

Amazon has already provided a free slot for each of the services that we have done. With Amazon SageMaker, however, I have not seen that.

I have not yet explored everything, but they are doing good work.

In terms of the dashboard, I can say that I have not explored the visualization aspect very much, but they have their tools. I don't know how flexible it is and how much customization you can do. That's something on the visualization side that I don't enjoy very much. My interests are mostly towards data engineering or data science.

I would rate this solution a nine out of ten.