Amazon SageMaker Reviews

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.

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 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.

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.

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 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 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.