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.

The primary use cases for Amazon SageMaker are for EDA processing, ML model building, setting up MLOps, predictive analysis, customer churn models, fraud detection, image and video analysis, as well as NLP projects. It is a versatile tool in the machine-learning landscape.

The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities. These features allow for generating high-quality models without needing extensive coding knowledge, making it accessible for non-experts. SageMaker helps in end-to-end machine learning, incorporating data preparation, model deployment, and continuous monitoring.

Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker. It is considered complex to integrate these aspects, and adjustments need to be made in multiple places, which should be more user-friendly. A centralized interface for managing these configurations is desired.

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

Amazon SageMaker's stability depends on how well-configured the entire setup is. Due to the interconnected dependencies within the system, the learning curve may be steep for new users. However, with proper configuration, the overall stability is adequate.

Amazon SageMaker offers a high level of scalability. It allows dynamic resource allocation and supports large datasets through various features like multi-model endpoints and flexible instance configuration, scaling up or down according to requirements.

Technical support for Amazon SageMaker involves communication through web chats or telephone based on the support agreement.

Positive

In AWS, we used to build the whole pipeline model by ourselves, component by component. With Amazon SageMaker, costs have been optimized as it includes pre-configured components that reduce overall expenses.

The initial setup of Amazon SageMaker can be achieved quickly if the default configuration is used. However, setting it up more customizable, such as for specific requirements, can make the process time-consuming, earning an eight out of ten in terms of ease.

A single knowledgeable person with expertise in ML and cloud can handle the deployment and maintenance of Amazon SageMaker.

We have seen a significant reduction in costs using Amazon SageMaker. Building any ML lifecycle benefits from SageMaker's pre-configured components, which bring down the overall cost compared to setting up all components separately.

While Amazon SageMaker is expensive compared to other cloud vendors, certain cost optimizations can be made with proper setup and configuration knowledge. Greater visibility from AWS regarding cost-impacting configurations would be beneficial.

No other solutions were evaluated outside of AWS, as we were setting everything up within AWS before opting to use Amazon SageMaker.

I rate SageMaker eight out of ten. 

New users should conduct a pilot or proof of concept with Amazon SageMaker to see if it aligns with their business use cases. Evaluate and understand the integration with other AWS services and ensure the team has adequate knowledge to handle monitoring, model performance, and managing costs efficiently. Engaging with the community to remain updated on any misconfigurations is also advisable.

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.

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.