Amazon SageMaker Reviews

I use Amazon SageMaker for data preparation and machine learning. We create an automated pipeline to ensure datasets are prepared every day, conduct model training, and monitor and optimize models along with making predictions.

It helps us monitor our models on a daily basis, allowing us to check the performance with new data, compare different models, and replace or optimize non-performing ones. It also assists with feature lineage, enabling us to track features throughout the pipeline.

The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline.

The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably.

I have worked with SageMaker for three or four years.

There have been incidents of downtimes. That said, I am not aware of the frequency as I am not in charge of monitoring. These occurrences cause some consequences, but they rarely impact daily operations critically.

SageMaker is highly scalable, with a rating of ten out of ten, as it effectively handles a large volume of daily data, helping with data preparation and prediction integration.

The technical support team is very helpful, with a rating of nine out of ten. They assist us well with resolving service issues and provide valuable advice.

Positive

The initial setup and deployment were handled by another team within the organization.

I'd rate the solution ten 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.

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.

We use the product for deploying machine learning models. We use it for the machine learning model development process.

We're currently implementing a project on a cross-selling model. It is like a standard XGBoost model. I’m evaluating the tool to see whether it will improve the workflow.

SageMaker Studio sounds very interesting. Feature Store and data pipeline features are very interesting. The product is a one-stop shop. It allows people without much engineering knowledge to try out and deploy models in environments similar to the production environments. The tool makes our ML model development a bit more efficient because everything is in one environment. It is easy to manage compared to when things were in different components of AWS. Amazon SageMaker is in AWS, so I need not pay two bills. It is one less system to manage, so it is easier.

The product must provide better documentation. I don't see a lot of documentation, particularly on the Studio feature. In general, there is not a lot of information about how to use Feature Store. I can see it there, but the documents are not very explanatory.

I have been using the solution for two years.

The product is stable. I rate the stability an eight out of ten.

The product is very scalable. I rate the scalability an eight out of ten. Six people use the product in our organization. We are planning to increase the usage.

We are premium AWS customers.

Positive

I have used Databricks before. From a feature perspective, Databricks is better than SageMaker. The user experience of Databricks is much better. SageMaker’s advantage is its cost. The data is already on AWS S3. It’s less of a hassle. SageMaker is convenient. Databricks is like a MacBook. I can still work with SageMaker, but it is not as pretty, and the flow is not as natural as on Databricks.

The setup is not very easy. I rate the ease of setup a seven out of ten. The deployment takes ten minutes.

The pricing is comparable. It is not very cheap. I rate the pricing an eight out of ten. The main reason why we're using it is because of its cost. We are aiming at keeping the costs at $100 per month.

The product can scale model training and deployment. It is one platform. It is easy to use. People who want to use the product must first focus on defining the workflow of their team without any tools and then see how the product adapts rather than trying to use all the features of the system. It can confuse us. 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.

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.

I use SageMaker to use a "bring-your-own-model" setup. For SageMaker AutoML, we're fine and happy with it. It is restricted because you can't move through multiple algorithms. It seems to work only with two. One of the things I am doing is prototyping, and it's proving quite difficult to get our model working how we want it to. It's proving complex with many moving parts, and the documentation is only partially helpful. SageMaker requires a lot of work to get it working.

I spent the last four months trying to get a prototype working and exploring to bring in a model while exploring alternate models and making prototypes work. We've stepped back to AutoML for now. We might be using EKS, so we bring our containers. Within the containers, we can work with what we need to work with.

The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework. When you couple it with step functions, you can do some very powerful things. It manages deployment, you have model monitoring, and you have model quality checks. It's got a lot of end-to-end services one needs to get a full machine-learning pipeline running. While I say that I had a struggle and blame the product partially, I am also impressed with the ecosystem. I would still use it over and above other competing products, but I don't know the Google setup. I have worked very briefly with Azure, so I can't do a proper card-to-card comparison, but I do like the ecosystems AWS brings. If a client came along and asked me to set up a machine learning ecosystem, a full machine learning production deployment, I would use Sagemaker.

The solution is complex to use.

Some additional functionality would be for them to provide sample end-to-end card formation templates and try to unify the setup. At the moment, as you move from one set of documentation to the next, some of the documentation is for bringing your model, some of the documentation is for SDK, some of it's for API, some of it's for command lines, and some of it's for step functions. None of the documentation seems to be end-to-end. There are gaps in the documentation. It proves to require a lot of digging from the user to figure it out. I did get through the AWS machine learning specialty certification, but that proved to be a bit superficial. Though it covered a lot of ground, it didn't have the detail one would need for Sagemaker. I am self-taught in a lot of the stuff. I could dive deeper into some code and take time to get examples running. But I was consulting a startup, and they needed to move quickly.

I was hoping SageMaker would be easier to work with because I was expecting there would be examples we could repurpose that were more complete.

The new functionality I'd like to see is Amazon tuning attention to the documentation sets and the templates.

I've been using Amazon SageMaker for about two and a half years.

Stability is not a relevant metric anymore because SageMaker runs on its own underlying AWS serverless infrastructure, which is 100% reliable.

Folks better than me with more extensive resources and time have run and checked SageMaker ten times a second every second for 1,000 hours to see if they got a drop, but they haven't. It's serverless and bulletproof.

I rate the scalability a ten out of ten. Part of the problem is that AWS has limited the functionality of SageMaker in many ways to make it scalable. So it's scalability first and then functionality second.

The solution works well for medium-sized businesses and up. But even for small businesses, you can do some simple and quick elastic endpoints and get going quickly. The problem is the amount of work it takes for people to know what they're doing with Sagemaker, and those people are probably rare. I've been able to get things up and running in most cases in all sorts of AWS services, but I'm struggling. Small, medium, and large enterprises could use SageMaker with an automatic model, but it depends on the people's skills doing the deployment. A small business probably couldn't afford contractors, consultants, or data scientists. It's not about AWS. It's a problem with classic data science skills.

I rate the initial setup a three or two out of ten because it's very complex.

You don't pay for Sagemaker. You only pay for the compute instances in your storage. SageMaker is free.

I've had a little bit of a look at Azure, but I didn't get into the level of detail I did with Sagemaker. I have worked reasonably intentionally with DataRobot and H20. But SageMaker is a way bigger, way more capable platform. The AutoML is very simple, and it is much, much cheaper. The cost of SageMaker is nothing. By contrast, if you're using DataRobot, you'll pay $100,000 plus for a five-year license.

Anyone doing on-prem at the moment for anything but their core datasets or legacy systems that can't be moved is just paying useless money.

I rate Amazon SageMaker a seven out of ten. I'd recommend it to other users. It's worth syncing the time and effort into getting it running.

We use the solution to extract financial information and contractual data from unstructured documents.

The product provides the ability to develop AI models relatively quickly. My team develops the models using the tool. We use AI quite extensively in our business. We use the tool for predictive analytics. It helps predict which trade might fail based on historical data. Automatic Model Tuning helps improve the productivity of the investment operation team. Typically, an analyst spends about 45% of their time collecting, organizing, and ingesting data. We were able to use the product to automate processes.

The solution requires a lot of data to train the model.

I have been using the solution for the past 12 months.

The tool’s stability is pretty high. I rate the stability a nine and a half or ten out of ten.

The scalability is very high. I rate the scalability a nine out of ten. We are a small team of AI analysts. We have half a dozen users.

The support is usually pretty responsive. The solution has a fair bit of content online. We haven't had any support challenges.

Positive

We were using Azure’s tool before. We switched to Amazon SageMaker because it allows us to sell it to larger institutional clients. AWS is more prevalent in the broader institutional segment.

The initial setup is relatively straightforward. The same team developing the models deploys and tests the solution. The tool requires a bit of ongoing maintenance. It is relatively easy to do.

The product is expensive. I rate the pricing a five or six out of ten.

We are partners and resellers. Overall, I rate the product a nine out of ten.