Amazon SageMaker Reviews

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.

I am currently working with AWS services like SageMaker, S3, EC2, VPC, load balancing, auto scaling, and RDS. SageMaker is used primarily for AI projects to build, train, and deploy AI models from scratch.

SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project. It provides lifecycle configurations, similar to EC2's user data, for running scripts on instances. It also offers VPC features to isolate SageMaker instances when needed, which is a valuable use case.

The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement.

I have been working with SageMaker for about one week due to a project related to AI.

I have not encountered any stability issues with SageMaker so far.

Scaling SageMaker has not been an issue. If required, I can increase the instance size to a higher tier as needed.

I haven't needed to contact AWS technical support for this project.

Positive

When I first started working with SageMaker, I was unfamiliar with it. I consulted AWS articles and searched online to understand how SageMaker works.

I was responsible for deploying SageMaker for my client and configuring notebook instances with VPC and subnets.

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

I recommend SageMaker for ML projects if you need to build models from scratch. If you do not want to maintain the instances, consider using Datablock instances. On a scale of one to ten, I would rate SageMaker a nine.

In terms of the tool's use case, if it is serverless, and if the compute involved is not too high, or if it is a PoC kind of a thing, and you want the microservices kind of architecture to be going and go for a pay as you go model, you can use the tool. With the tool, you know what is happening, so maybe you can cut costs by going with an on-premises model and having a stable system for computing.

The models that the tool has, the libraries, and the ML libraries are rich. That is good, and that is one of the features of the tool. The other feature is how the tool interacts with other components of AWS, like Lambda and S3. It's seamless if you are using AWS architecture.

I was working with Amazon SageMaker Ground Truth. The serverless feature is important and worth it because you don't have to spin up an EC2 instance every time or a server. For us, the main attraction is that it's serverless. You pay only for the compute that you use, and then there is its ecosystem. Whenever you use something in AWS, the ecosystem is very rich.

For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads. In EC2, you have spot instances that cut costs tremendously, but you don't have that in Amazon SageMaker. You pay for the local usage. I would like to see better integration with GPUs. GPUs are very expensive for AWS or any cloud provider. NVIDIA has introduced options with Databricks for GPUs, so it would be interesting to see how Amazon SageMaker can parallelize GPU usage. I haven't used it to scale multiple GPUs automatically for model training. The key points are the cost and how effectively they integrate GPUs into the workload for training machine learning models. We want to see how seamless it is and how it can work. I haven't used multiple GPUs scaled automatically. For model training, the first concern is cost, and the second is how effectively they want to integrate GPUs into the workload for training machine learning models.

I have six to seven years of experience with Amazon SageMaker.

After production, a different team handles the tool. I would be there till the PoC phase and then after production we move on to other projects. From my standpoint, I rate the solution's stability a nine out of ten. I don't continuously maintain production workloads, as it is managed by a different team.

The tool is extremely scalable. If you go on and use it for the entire life cycle of ML, it's very expensive. I say it's scalable, but it's expensive. For a production project, I would think that deployment and inferencing can be in SageMaker. We will have to, at some point, move other stuff to a less costly thing. It is very scalable.

My clients are mostly mid-level to enterprise businesses. I have worked with huge clients, but our clients may not be that huge. When we worked as a product engineering partner to AWS for two years, I dealt with huge clients, but that was very specific. I was working with Amazon SageMaker Ground Truth. I have worked with Nissan, Sony, and Samsung, but even though it was ML, it was a very niche kind of thing. We were doing labeling support for the ML models and training data. We were doing training data and labeling by partly using ML. Sometimes, we used to use Amazon SageMaker as well, but that was very niche. If we were to embark on a complete ML journey with some clients, then I would say that I have dealt with small to mid-scale customers.

The technical support of the tool was good. I rate the technical support a ten out of ten.

Positive

My company takes a tool-agnostic approach and utilizes a variety of AI and ML tools across the ecosystem, focusing on Amazon, Microsoft, Databricks, and Snowflake.

Speaking about the product's initial setup phase, I am in an Amazon ecosystem. I find the tool good because it depends on whether you are very command-line or user-interface-driven. If you are user interface-driven, I think Azure is good. I am okay with the command lines and shell kind of an interface. I am more used to AWS than other cloud providers. I think it's good for me. For a programmer, it's easy. As a technical person, it's easy. As a business person, I think Azure is a little easier than AWS.

The tool is deployed on a private cloud. Sometimes, we don't use it ourselves and then just give access to the apps to the client, meaning we host it. We host the model, and then the clients use the model but don't have access to infrastructure. We mostly used AWS along with some other cloud providers too.

The tool's deployment was fast, and it took a week.

For the deployment, whatever we do in general for on-premise, we do almost the same thing in SageMaker. Just that it offers parallelism. We do EDA and all that. We first have the data in the S3 bucket, and then we do the EDAs. Then we do the training. Then, we do the grid optimization. First, we mostly start with the pilot. When I say that deployment takes one week, it's just for having it up and running and showing some results with the model. It's not for the complete production model, as it is a different cycle. We start the PoC with the pilot model first, and then we kind of do it. Whatever steps are involved for on-premises, we do the same steps for SageMaker. Just that the computing cost is less, and we don't have to spin up a server. For the rest of the steps, like getting and cleaning the data, we don't do a lot in SageMaker. We kind of do it outside, or we do it with EC2 instances or use PySpark, maybe, and Databricks because that is a little easier compared to SageMaker libraries. You can do it, but it's kind of expensive. We do all that data transformation, and then we do the modeling. Most of the time, 100 percent deployment is done in SageMaker, but the rest of the parts are a mix of technologies. There is an influenced pipeline.

The cost offers a pay-as-you-go pricing model. It depends on the instance that you do.

I used Amazon SageMaker as a customer from the client side, though I have worked as a contractor with Amazon for two years. I work for Persistent, but the engagement with our client was over last year. When I was working at Amazon for two years, I was working at Amazon. Now, we are partners as well. We are strategic partners for Amazon, Microsoft, Google, Databricks, Snowflake, and most of the ecosystems.

If you want to try out something with, say, for instance, you want to do a PoC for testing more, I mean, say, for instance, that you're doing a data annotation project. You want to see how it goes. You don't want to invest a lot, and you want to try it out and see whether it works or not. For those kinds of typical PoC situations, I would say Amazon SageMaker is good. You are using a microservices architecture, and you want to go serverless. That is the first use case. The second use case is that you want to go serverless and plug and play a lot of components rather than having a bulk of computing like EC2 and all that. You would rather have an Amazon setup that is serverless.

We use it for tuning, but it's just like any other tool, except for the fact that it's serverless. It's not that it significantly boosts anything; it's just a choice. Either we tune it on-premise or we tune it on the cloud. We use Azure, AWS, and all that. So, in terms of tuning, it's not special. It's just the way you tune any model in any environment, and that is not a huge thing. It is a good tool that works well with its components and other components. There's nothing special about the tuning itself. You can either use PySpark or other cloud technologies. It's not that we get a huge boost just because it's AWS.

The serverless feature and the complete lifecycle that can be handled inside SageMaker are important. It covers everything from training the model to deploying it and sometimes using it for data pipelines. However, we generally don't use it for pipelining and data transformations because it's expensive inside SageMaker. We do use it for model training, although sometimes we train outside. We also utilize model training and Amazon SageMaker JumpStart, which is pretty handy because you don't have to train the model from scratch. You can use it, especially for LLM settings, right out of the box. There are models inside SageMaker that make it a little faster, both from a computing perspective and from a bandwidth deployment perspective, so you don't have to spend a lot of time training before deployment. Amazon SageMaker JumpStart is definitely valuable, along with the whole lifecycle for ML as well.

I would recommend others if they want to do a quick PoC workload, or proof of concept, and if they want to do something very quick, then I would definitely recommend it. If it's a very huge production workload, then I might want to consider other options. But for anything where there is a PoC kind of thing, I would recommend products in such areas.

Speaking about AI, I can say that it's kind of quick to set up and get it running. I can't say specifically. We have worked on a lot of projects. We have worked with document processing projects a lot. In those cases, if you were asking about specific projects, I can remember a recent project where we were trying to digitize documents using manual annotation and automated ML models, but there, we didn't use SageMaker. We used Amazon SageMaker Ground Truth, which is under the umbrella of SageMaker. If you use Ground Truth, it's a SageMaker product. We were using SageMaker Ground Truth, which is pretty handy because it sits well in the environment. If you are specifically asking how it accelerated the process, it was easy to set up, and we just got going in less than a week. So, yeah, I can think of that example.

I rate the tool an eight out of ten.

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. 

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.