Amazon SageMaker Reviews

I sit with our stakeholders, analyze the data, and then we come up with the solution, whether it goes to traditional machine learning or AI. We are dealing with one of the financial companies for document pre-processing. Normally they are opening the document and doing manual work, such as identifying the loan numbers, ID numbers, and all of that. We are using AI to scrape the document and pass our system prompt to automatically take the IDs in the UI itself. You just need to upload the document to the system. Amazon SageMaker will be analyzing that document and then coming up with the IDs and what IDs are present in the document.

There will be many features in Amazon SageMaker itself, but we don't know whether the feature is there or not, particularly the documentation part. Whatever the new releases will be, they will not post very fast.

It is very easy to deploy Amazon SageMaker. The documentation is also very good.

It is good because we are able to collaborate with our notebooks. At a time we can develop simultaneously and work on different use cases in the same notebook itself.

It takes some work. We need to refer to the documentation. The documentation is good regarding what other providers we are able to connect with. Out of five, I can say 3.5.

I have been using this for three years now.

Amazon SageMaker is definitely stable.

It depends upon the model, basically. On average, the normal models take around 10 to 15 minutes of time.

It takes some amount of time for them to solve the ticket.

Comparatively, GCP offers very low cost when compared to Amazon SageMaker. People are moving from Amazon SageMaker to GCP because of the cost constraints. The same thing will be offered at a very less price in GCP. In AWS, it is more costly.

It is very easy and, out of five, I would rate it 4.5.

The implementation team definitely helped with this solution.

Amazon SageMaker definitely provides ROI. Previously we benchmarked it at around 9.8% or something like that.

Positive

My primary use case for Amazon SageMaker revolves around setting it up for our data scientist users. I am involved in the provisioning part of it, working on tasks like security setup and infrastructure, akin to an SRE role. It is mostly utilized for provisioning SageMaker for data scientists in my organization so they can utilize the tool.

Amazon SageMaker's sharing capability with the Feature Store is something I find valuable and is actively used by users in our environment. The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable. The infrastructure manages multiple notebook instances, providing flexibility. However, initial setup time might be slow due to spinning up of GPU or EC2 instances. 

Compared to classic notebooks that have one instance per user, Studio allows for multiple notebook instances, despite certain limitations like the lack of no-code and low-code capabilities.

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. 

Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

I have been working with SageMaker for more than four years.

I rate the stability of Amazon SageMaker between seven and eight. It is fairly stable despite occasional challenges with frequent UI changes.

Amazon SageMaker is scalable and works well from an infrastructure perspective. I would rate its scalability eight out of ten.

The technical support from AWS is excellent. I would rate it nine out of ten.

Positive

The initial setup for our type of infrastructure was complex as we had to restrict instance types and manage user domains. However, once the initial setup was completed, the system became seamless to use.

Pricing for Amazon SageMaker is high. That said, organizational discounts are available. For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.

Competing products to Amazon SageMaker include DataIKU and Databricks. These tools offer features that compete with SageMaker.

My overall rating for Amazon SageMaker would be seven out of ten. I recommend it to other users depending on the use case. 

For solution stability, I would rate it between seven to eight out of ten. For scalability, it would be an eight out of ten. Amazon SageMaker is suitable for companies with medium to large user bases, around 400 to 1,000 users.

The primary use cases of this solution include designing, deploying, testing, and evaluating machine learning models. It is also utilized to develop ML models quickly, operationalize them effectively, and deploy those models in production.

The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models. 

Additionally, the AutoML capability is beneficial. These features facilitate rapid development and deployment of AI applications. The pricing and performance efficiency of the solution is better compared to some alternatives. SageMaker provides easy deployment, rating an eight on a scale of ease.

One area for improvement is the pricing, which can be quite high. Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.

I used SageMaker two years ago, having three years of experience with it.

Stability was rated as good, with a score of seven to eight out of ten.

The scalability is considered good. However, the availability of GPU instances can be a challenge, requiring proper planning.

The support is very good with well-trained engineers whose training curriculum is rigorous.

Neutral

While previously using SageMaker, I have moved to Google's AI ecosystem for most of my tasks.

The initial setup is easy, scoring an eight, making it straightforward for users.

The implementation involves ML engineers, data scientists, and ML operations engineers who understand the underlying algorithms.

The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.

The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.

I have used both Amazon SageMaker and Vertex AI in my work.

On a scale of one to ten, I would rate SageMaker at eight out of ten. Depending on the existing ecosystem, it can be recommended, and monitoring supply constraints is advised for GPU instance usage. 

In our data science projects, we have a whole range of uses with Amazon SageMaker. We use it for anything from predictive asset health through to computer vision, along with Bedrock and some of the new AI services.

We don't use Amazon SageMaker for AI; we use Bedrock and other tools.

The tangible benefits we have observed from using Amazon SageMaker include improved time to insight and generally the common stack that is easier to support over time.

The product has been stable and scalable.

Their experience could still improve around scaling for ops and scaling operations. There aren't many people that know how to do that effectively. We're not really looking for support so much on how to solve problems anymore; it was more about how to scale and manage it across our different data science teams.

They could add features such as managing environments, experiment management across those environments, and the integration with training datasets as you go through those experiments. Not all those things sit in Amazon SageMaker; they sit outside, and that's a challenge that AWS has to think about more broadly. It's the actual data landscape, not the ML products, that's the problem.

I have been working with Amazon SageMaker for about six years.

We faced difficulties with implementation. When we first built our first lake, there weren't really any lake services. It was all microservices and containers. None of the lake services that AWS has today existed back then, so we've got a pretty complex and fragmented environment.

Parts of our team have been satisfied with Amazon SageMaker generally.

The product has been stable and scalable.

Their experience could still improve around scaling for ops and scaling operations. There aren't many people that know how to do that effectively. We're not really looking for support so much on how to solve problems anymore; it was more about how to scale and manage it across our different data science teams.

Technical support for Amazon has been pretty good; our account team depends on the challenges we have faced. The issue is not so much their support as the fragmented nature of their services.

I would rate their support for Amazon SageMaker a seven out of ten.

Positive

We've used a hodgepodge of solutions historically with our existing data infrastructure, basically lakes we've built in each cloud. We're actually moving all of that to Databricks at the moment.

We've had third parties help us manage these issues, but we've also had our own teams; it's a combination.

We have seen a return on investment. It depends on the different projects. Some do, some don't, depending on which ones we've been using.

If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.

We're okay with our current solutions.

Amazon SageMaker is one of the products we've been dealing with along with Azure ML.

I manage a data science team and our machine learning and AI infrastructure, so I have not been using it myself as a consultant or developer.

For machine learning itself, it's fine. The nuance is what the rest of your data infrastructure looks like and what other platforms you have. Particularly with AI ramping up, you have to go to another service again, so it depends on the types of solutions that you're actually trying to tackle.

For machine learning, I would give Amazon SageMaker a seven or eight out of ten.

I would recommend Amazon SageMaker to others.

Overall review rating: 7/10

I primarily use Amazon SageMaker for analytics on cold calling and analyzing financial numbers. Additionally, it helps me connect with customers in real-time using chatbots.

I appreciate the telemarketing analytics and integrations with Chime SDK. They offer insights into everyone making calls in my organization. This allows monitoring and performance grading, as I instantly know when someone has a bad call. It also allows me to gain insights from data.

The main challenge with Amazon SageMaker is the integrations.

I have been using Amazon SageMaker for about a year.

I have never had to contact their support team.

Neutral

The initial setup requires some experience and knowledge of AI.

I have seen a return on investment, probably a factor of four or five.

Amazon SageMaker is expensive, costing close to $1,000 a month.

I recommend learning about AI and studying the AWS machine learning specialization. 

I would rate Amazon SageMaker an eight on a scale from one to ten.

The primary use case for Amazon SageMaker involves developing on the cloud to utilize flexible compute resources. I use it when dealing with really large datasets that do not fit on my local laptop. I leverage SageMaker's more powerful GPU, CPU, and memory for these tasks.

Amazon SageMaker, in combination with Posit, helped develop an internal application that aids our business partners in prioritizing workflow, saving us approximately three million dollars.

The most valuable feature of Amazon SageMaker is its flexibility in selecting resources according to my needs. It is not a one-size-fits-all solution and is highly adaptable to my requirements. 

Additionally, the intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook. Its integration with our Snowflake environment also contributes to its overall ease of use.

There are two areas in SageMaker that need improvement. 

Firstly, when starting a new session, the waiting time can be quite long, ranging from two to five minutes. 

Secondly, the integration with Snowflake is not optimal. I face challenges in authentication, requiring multiple steps to achieve single sign-on. This process feels somewhat cumbersome.

I have used Amazon SageMaker for about one and a half years.

Amazon SageMaker has been quite stable overall for the past one and a half years. I cannot recall an instance where it broke down.

The scalability of SageMaker is highly impressive. Its compute resources are incredibly flexible, allowing us to scale from a basic setup to one with multiple GPUs and extensive RAM, depending on our requirements.

The technical support for Amazon SageMaker is not very responsive. It takes time for them to respond, and setting up a meeting with a support person can be challenging. Moreover, they often request additional fees for more attentive support.

Neutral

Previously, we used an Azure Kubernetes cluster to host Posit Workbench. We switched due to adding new team members skilled in AWS and to reduce infrastructure costs, which were over one hundred thousand dollars annually with Azure Kubernetes.

The initial setup was rated eight out of ten. It was straightforward for the tech team to install Workbench and SageMaker, following AWS documentation. However, configuring security roles and access for different users was more complex.

I worked with the tech team for the initial setup, and they found the process very straightforward using AWS's guidelines.

The use of Amazon SageMaker, along with Pawsit, led to developing an application that saved the organization about three million dollars.

Amazon SageMaker itself is relatively inexpensive, primarily charging for compute resources. However, there are additional costs for enhanced customer support services.

Amazon SageMaker is an excellent product for users already within the AWS ecosystem, but its integration capabilities outside AWS could be improved.

I'd rate the solution eight 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.

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.