# Amazon SageMaker Reviews **Vendor:** Amazon Web Services (AWS) **Category:** [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) **Average Rating:** 4.2/5.0 **Total Reviews:** 55 ## About Amazon SageMaker Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management. ## Amazon SageMaker Pros & Cons **What users like:** - Users find Amazon SageMaker’s **ease of use** exceptional, allowing quick adaptation and efficient model training without complexity. (3 reviews) - Users value the **seamless AI integration** of Amazon SageMaker, which streamlines the complete machine learning lifecycle efficiently. (2 reviews) - Users commend the **exceptional computing power** of Amazon SageMaker, enabling rapid training of deep learning models. (2 reviews) - Users value the **efficient training process** of Amazon SageMaker, significantly reducing time spent on deep learning models. (2 reviews) - Users love the **fast processing** of Amazon SageMaker, significantly reducing model training time from hours to minutes. (2 reviews) - Users value the **comprehensive managed Jupyter notebooks** and seamless integration with popular ML frameworks and tools. (2 reviews) - Implementation Ease (2 reviews) - Model Management (2 reviews) - Setup Ease (2 reviews) - Time-saving (2 reviews) **What users dislike:** - Users find that Amazon SageMaker can be **expensive** , particularly due to its complex pricing and long-running job costs. (3 reviews) - Users find Amazon SageMaker's **pricing complex and potentially costly** , particularly for extensive training jobs and large deployments. (2 reviews) - Users find **complex pricing and steep learning curves** in Amazon SageMaker, leading to difficulties in managing costs effectively. (2 reviews) - Users find the **steep learning curve** challenging, particularly during the initial setup for Amazon SageMaker. (2 reviews) - Users note a **difficult learning curve** during the initial setup of Amazon SageMaker, complicating the onboarding process. (1 reviews) - Users find the **difficult setup** of Amazon SageMaker challenging, impacting their overall experience and cost estimation. (1 reviews) - Integration Difficulty (1 reviews) - Performance Issues (1 reviews) - Steep Learning Curve (1 reviews) ## Amazon SageMaker Reviews ### 1. Fully Managed End-to-End ML in AWS with Powerful Distributed Training **Rating:** 4.0/5.0 stars **Reviewed by:** Hem J. | Assistant Manager, Enterprise (> 1000 emp.) **Reviewed Date:** May 21, 2026 **What do you like best about Amazon SageMaker?** Amazon SageMaker’s biggest strength is that it delivers a fully managed, end-to-end machine learning (ML) environment within AWS’s secure ecosystem. It covers the whole workflow, from data preparation and model training to deployment and ongoing monitoring. For teams, this can save a huge amount of time by abstracting away infrastructure complexity, while still providing powerful distributed training capabilities, including features like HyperPod. **What do you dislike about Amazon SageMaker?** Opaque pricing. Costs can escalate quickly, especially with long-running jobs or larger deployments. I’ve also seen “month-end shock” because the billing model isn’t clear or predictable. **What problems is Amazon SageMaker solving and how is that benefiting you?** Infrastructure management It removes the need for me to provision and maintain servers, GPUs, or clusters. AWS takes care of scaling, availability, and fault tolerance, which makes the overall setup much easier to manage. ### 2. Amazon SageMaker: End-to-End ML Workflow That Gets Models to Production Faster **Rating:** 5.0/5.0 stars **Reviewed by:** Md R. | Strategy Specialist – Operations & Process Improvement , Small-Business (50 or fewer emp.) **Reviewed Date:** May 21, 2026 **What do you like best about Amazon SageMaker?** I like that Amazon SageMaker gives you one managed path from experimentation to deployment without forcing you to assemble every piece yourself. It covers the whole ML workflow: notebooks, training jobs, tuning, pipelines, model registry, endpoints, batch inference. It reduces infrastructure work: you can focus more on models and less on provisioning GPUs, containers, scaling, and endpoint ops. It works well for teams: reproducible training jobs, managed pipelines, and deployment/versioning help when multiple people touch the same system. It scales from simple to serious: you can start with a notebook and later move to distributed training or production endpoints in the same ecosystem. It integrates with AWS well: IAM, S3, CloudWatch, ECR, Lambda, and EventBridge make it easier if the rest of your stack is already on AWS. If I had to pick one thing: the biggest advantage is the operational glue—it makes moving from “model works in a notebook” to “model runs reliably in production” much less painful. **What do you dislike about Amazon SageMaker?** The biggest downside is complexity: SageMaker is powerful, but it often feels like a toolbox of AWS services rather than one clean, opinionated ML platform. The learning curve is steep; you end up needing to understand SageMaker itself plus IAM, S3, VPCs, ECR, CloudWatch, and AWS networking. Costs can get slippery; notebook instances, endpoints, training jobs, storage, and data transfer can keep running unless you manage them carefully. The UX can feel fragmented; some tasks are easier in the SDK, some in Studio, some in raw AWS configuration. Debugging can be frustrating; failures are often caused by permissions, container setup, networking, or obscure configuration mismatches rather than model code. Vendor lock-in is real; once your pipelines, deployment flow, and monitoring are built around SageMaker/AWS primitives, moving away takes work. The abstractions can be leaky; “managed” does not always mean simple, and you still may need to think like an infra engineer. If I had to sum it up: SageMaker is very capable, but it’s not especially elegant. It rewards teams that already operate comfortably in AWS, and can feel heavy for smaller teams or faster-moving experimentation. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker is mainly solving the “ML operationalization” problem: turning model development into something repeatable, scalable, and deployable. For me, the benefit is less about building a single model and more about reducing all the friction around it. It solves infrastructure setup for training and inference; instead of hand-building GPU servers, job schedulers, and serving stacks, you can run managed training jobs and endpoints. It solves workflow fragmentation; data prep, experiments, tuning, pipelines, model registry, and deployment can live in one ecosystem instead of a pile of disconnected tools. It solves scaling problems; you can move from small experiments to larger training jobs or production traffic without redesigning everything. It solves repeatability and team coordination; jobs, pipelines, artifacts, and model versions are easier to track than ad hoc notebook-driven work. It solves production deployment overhead; managed endpoints, batch jobs, and monitoring make it easier to serve models reliably. It solves AWS integration pain; if your data and apps already live in AWS, SageMaker reduces the glue code between ML and the rest of the platform. How that benefits me: I spend less time on DevOps-heavy ML plumbing. I get a faster path from prototype to production. I have a more standardized workflow for teams and projects. I can rely on managed scaling and monitoring instead of inventing it. I avoid stitching together many separate tools unless I want more customization. The tradeoff is that it benefits me most when I actually need that operational structure. If I just want lightweight experimentation, SageMaker can feel heavier than necessary. ### 3. SageMaker Brilliantly Scales Training Beyond Local Limits **Rating:** 4.5/5.0 stars **Reviewed by:** Sachin N. | Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** May 22, 2026 **What do you like best about Amazon SageMaker?** Working locally is great—until your dataset outgrows your RAM, or you realize you need a multi-GPU cluster to train a model in hours rather than days. SageMaker addresses that exact friction point, and it does so brilliantly. **What do you dislike about Amazon SageMaker?** I end up spending a massive amount of time digging through CloudWatch logs just to discover that a library version was mismatched or that an S3 file path was slightly off. It really drags out the debugging process and drastically slows down the inner loop. **What problems is Amazon SageMaker solving and how is that benefiting you?** By automating provisioning, scaling, and deployment, it keeps me from wasting hours dealing with CUDA drivers, server maintenance, or overly complex Docker pipelines. That means I can focus fully on data science and move models faster from a local idea into live production. ### 4. End-to-End ML Workflow in One Tool: Build, Scale, and Monitor **Rating:** 5.0/5.0 stars **Reviewed by:** Arshiya A. | HR Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** May 22, 2026 **What do you like best about Amazon SageMaker?** Best is how it helps in end to end working -model building, scaling, monitoring so that everything is done in one tool. **What do you dislike about Amazon SageMaker?** Everything is perfect, just needs to be informed to people more about it **What problems is Amazon SageMaker solving and how is that benefiting you?** It manages everything automatically, teams does not need different tools for the same. Data workflows are easy to manage along with experiment tracking ### 5. Bottleneck-Free AI Training Without Infrastructure Headaches **Rating:** 4.0/5.0 stars **Reviewed by:** Biswajit B. | Lead SRE, Mid-Market (51-1000 emp.) **Reviewed Date:** May 20, 2026 **What do you like best about Amazon SageMaker?** AI training without any bottlenecks or dependencies on the underlying infrastructure. **What do you dislike about Amazon SageMaker?** The UI should be more polished and intuitive, easy to use model registry **What problems is Amazon SageMaker solving and how is that benefiting you?** Plug and play AI training for LLM models with out much infrastructure bottleneck. It also integrate with other AWS offered services , which makes data in and out very easy. ### 6. Blazing Fast Model Training, Intuitive Experience **Rating:** 5.0/5.0 stars **Reviewed by:** Amrendra K. | Indigo squad Member , Small-Business (50 or fewer emp.) **Reviewed Date:** January 02, 2026 **What do you like best about Amazon SageMaker?** I use Amazon SageMaker for building a deep learning model, specifically an object detection model. It's a really great experience for me, especially because my laptop doesn't have advanced GPU support, and training a model would take around 7-8 hours. With Amazon SageMaker's virtual machine, training my deep learning model only takes 3-4 minutes. This platform is great, and even someone who has never used it before can adapt to it the first time and easily understand all the functionality given on SageMaker. I think the virtual machine of Amazon SageMaker is more advanced than the Microsoft Azure platform. It is more effective and less time-consuming. The ease of use is brilliant; I can easily adapt to this platform compared to Microsoft. The initial setup is very easy, and with single authentication, I have access to the resources I need for my work. In my view, I give it 10 out of 10. **What do you dislike about Amazon SageMaker?** This is great platform. I don't dislike this. **What problems is Amazon SageMaker solving and how is that benefiting you?** I use Amazon SageMaker to train deep learning models much faster, reducing training time from 7-8 hours on my laptop to just 3-4 minutes on SageMaker. It's easy to adapt even for first-time users. ### 7. Complete ML Platform That Makes Building, Training, and Deployment Faster **Rating:** 4.0/5.0 stars **Reviewed by:** Subrat Kumar S. | Data Specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** May 19, 2026 **What do you like best about Amazon SageMaker?** It provides a complete platform to build, train, and deploy ML models. It also makes the whole process easier and faster. **What do you dislike about Amazon SageMaker?** I don’t see any real disadvantages, but it does take a bit of initial training for developers to get comfortable using the platform. **What problems is Amazon SageMaker solving and how is that benefiting you?** It makes building ML models so much easier, thanks to its unified platform and the way it leverages integration capabilities. ### 8. Effortless Prototyping with a Developer-Friendly ML Training Platform **Rating:** 3.5/5.0 stars **Reviewed by:** Vaibhav R. | Full Stack Developer - BA4, Enterprise (> 1000 emp.) **Reviewed Date:** December 22, 2025 **What do you like best about Amazon SageMaker?** I like how easy it is to train ML models on Amazon SageMaker and conduct fast experiments. I can easily prototype and make changes to my ML models, and the training process is straightforward. All the logs are accessible, which helps in checking the training status and testing models. This makes experimenting and changing parameters directly in SageMaker efficient. **What do you dislike about Amazon SageMaker?** Better cost transparency can be there. also, there is a learning curve with initial setup. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker gives us a single destination to train, deploy, and scale ML models. It reduces the need for separate management, making it easy to prototype and experiment quickly. ### 9. Effortless Login and Simple Setup Make It a Winner **Rating:** 4.0/5.0 stars **Reviewed by:** Pawan N. | Administration and Operations Assistant, Consumer Goods, Enterprise (> 1000 emp.) **Reviewed Date:** December 20, 2025 **What do you like best about Amazon SageMaker?** The login process is straightforward, and setting up the software is not complicated. The user interface is also very user-friendly. **What do you dislike about Amazon SageMaker?** The portal could use some additional finishing touches to appear more presentable. **What problems is Amazon SageMaker solving and how is that benefiting you?** The process of collecting and annotating financial documents is handled efficiently. I found the data collection to be thorough, and the annotation work is accurate, which helps ensure the quality of the financial data. ### 10. Accelerating Machine Learning Workflows Using AWS SageMaker **Rating:** 4.0/5.0 stars **Reviewed by:** NATARAJ M. | Student, Mid-Market (51-1000 emp.) **Reviewed Date:** July 03, 2025 **What do you like best about Amazon SageMaker?** What I like best about Amazon SageMaker is its end-to-end support for the entire machine learning lifecycle. From data preparation and model building to training, tuning, and deployment, everything is seamlessly integrated into one platform. I especially appreciate the built-in algorithms, Jupyter notebooks, and automated model tuning (Hyperparameter Optimization). The ability to scale training jobs easily and deploy models as fully managed endpoints with a few clicks or lines of code is a huge productivity boost. SageMaker Studio also provides a great collaborative environment for teams. **What do you dislike about Amazon SageMaker?** While Amazon SageMaker is powerful, one downside is its complexity and cost for beginners or small-scale projects. The learning curve can be steep, especially when configuring resources, managing permissions with IAM, or understanding the pricing model. Some features, like SageMaker Pipelines or Studio, can feel overwhelming without prior AWS experience. Additionally, debugging failed training jobs or deployments can be challenging without detailed logs or clear error messages. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker helps solve the problem of managing the end-to-end machine learning workflow at scale. It eliminates the need to manually set up infrastructure for model training, deployment, and monitoring, which saves a significant amount of time and effort. For me, it streamlines model experimentation, automates hyperparameter tuning, and enables easy deployment of models via scalable endpoints. This allows me to focus more on model performance and data quality rather than operational overhead. SageMaker also supports integration with other AWS services, making it ideal for building production-grade ML pipelines in a secure and compliant environment. ### 11. Flexible Billing Options and Smooth Mineral Tree Integrations **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Fund-Raising | Mid-Market (51-1000 emp.) **Reviewed Date:** May 21, 2026 **What do you like best about Amazon SageMaker?** Many options on bills when imputing or importing. Integrations into Mineral Tree work well. **What do you dislike about Amazon SageMaker?** Expensive for us to use, performance is well. **What problems is Amazon SageMaker solving and how is that benefiting you?** Simplifies transitioning models from development into production ### 12. Power of Machine Learning **Rating:** 4.0/5.0 stars **Reviewed by:** Shivani S. | Cloud Administrator, Small-Business (50 or fewer emp.) **Reviewed Date:** July 04, 2025 **What do you like best about Amazon SageMaker?** Amazon SageMaker supports the full machine learning workflow—from data preparation to model deployment—in one place. We can easily load data, explore it, train models, and test them without switching tools. I really like that SageMaker manages the servers for us, so we don’t have to set up or maintain any infrastructure. It also makes deployment flexible and simple. Overall, it makes ML projects much easier to manage, especially when working in a team. **What do you dislike about Amazon SageMaker?** It can be hard to learn at first, especially for beginners. The interface is sometimes slow or not very smooth, especially with large files or when switching tabs. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker helps me train and deploy machine learning models faster and easier. It gives ready-made tools, so I don’t have to build everything from scratch. This saves time, reduces errors, and helps me focus more on learning and improving my models. ### 13. A powerful platform for building and deploying ML models efficiently **Rating:** 4.5/5.0 stars **Reviewed by:** Gilbert G. | IT Manager -CTO/CISO, Enterprise (> 1000 emp.) **Reviewed Date:** July 01, 2025 **What do you like best about Amazon SageMaker?** End to end , Scalability and flexibility , Integration with AWS , ease of use , Model monitoring and debugging **What do you dislike about Amazon SageMaker?** Cost management , challenging to customize or go beyond the pre-built functionalities , documentation clarity , A good understanding of ML and AWS is needed to fully utilize its capabilities **What problems is Amazon SageMaker solving and how is that benefiting you?** The End to End workflow support handles everything from data prep to deployment. This means we can ingest, explore, train and evaluate models with just one environment to work it. I also really like that SageMaker takes care of the infrastructure, so we dont have to worry about setting up or managing servers. When it comes to deployment, it's very flexible ### 14. Excellent **Rating:** 5.0/5.0 stars **Reviewed by:** Ranisha R. | Teaching Assistant, Mid-Market (51-1000 emp.) **Reviewed Date:** May 25, 2025 **What do you like best about Amazon SageMaker?** What I like best about Amazon SageMaker is its ability to manage the entire machine learning lifecycle in one integrated platform. It simplifies model building, training, and deployment while offering scalability and powerful tools like SageMaker Studio and automated model tuning. **What do you dislike about Amazon SageMaker?** What I dislike about Amazon SageMaker is that its pricing can be complex and quickly become expensive, especially for long-running training jobs or large-scale deployments. Additionally, the learning curve can be steep for new users unfamiliar with AWS services and configurations. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker solves key problems in data science and machine learning, such as managing infrastructure, automating model training and tuning, and streamlining deployment. This benefits me by accelerating the ML workflow, reducing time spent on setup and DevOps, and enabling faster experimentation and production-ready model delivery. ### 15. Best ML tool there **Rating:** 4.0/5.0 stars **Reviewed by:** Pranav A. | Senior Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** June 10, 2025 **What do you like best about Amazon SageMaker?** Offers managed Jupyter notebooks (SageMaker Studio, Studio Lab), supports popular ML frameworks (TensorFlow, PyTorch, MXNet), and provides tools for distributed training and hyperparameter optimization. **What do you dislike about Amazon SageMaker?** SageMaker is expensive, especially for long-running training jobs, large-scale deployments, or when using high-performance instances. The pay-as-you-go model can lead to unexpected costs, and the pricing structure can be complex to understand and optimize. **What problems is Amazon SageMaker solving and how is that benefiting you?** SageMaker seamlessly integrates with other AWS services like S3 (for data storage), Lambda, Redshift, EMR, and Glue, simplifying data pipelines and workflows. ### 16. Machine Learning Tool **Rating:** 4.5/5.0 stars **Reviewed by:** Neeraj J. | Technical Manager, Enterprise (> 1000 emp.) **Reviewed Date:** July 04, 2025 **What do you like best about Amazon SageMaker?** No code & Infra headaches. Fully Managed e2e. **What do you dislike about Amazon SageMaker?** Cost complications and pricing. Migration in other cloud is bit challenging. **What problems is Amazon SageMaker solving and how is that benefiting you?** Auto ML for rapid prototyping. ### 17. Powering the Potential of AWS SageMaker in Data Science Projects **Rating:** 4.5/5.0 stars **Reviewed by:** Muhamamd U. | Individual, Retail, Small-Business (50 or fewer emp.) **Reviewed Date:** August 23, 2024 **What do you like best about Amazon SageMaker?** It is highly scalable, very compute-powerful, very well integrated with most vendors' data warehouses and data lakes, and can be accessed in the browser. **What do you dislike about Amazon SageMaker?** I can hardly make an estimate of the price calculation. Even though there is some tool called AWS pricing calculator, the list of available configurations doesn't show the number of configurations you can select while setting up the tool Studio and Notebook instances. **What problems is Amazon SageMaker solving and how is that benefiting you?** I use AWS SageMaker daily for data science projects, where Studio and Notebook instances are mainly used as a prime development environment. Now, what makes this tool ideal, due to its being in the cloud, is that you can work around large volumes of data with the ability to scale and have more resources as needed with a simple click. ### 18. The infrastructure is taken care **Rating:** 5.0/5.0 stars **Reviewed by:** Krishna K. | Senior Consultant, Small-Business (50 or fewer emp.) **Reviewed Date:** August 01, 2024 **What do you like best about Amazon SageMaker?** Provision of built in Algorithms and framework. Lot of the times, it's the data that causes the issues with the predictions. When we got the data right the predictions based on the built-in Algorithms did a great job in linear, logistic, classification techniques. Collaborative with other data scientists. It is easy to integrate with other related systems like Salesforce when we have our data in S3 buckets and the customer support is very responsive. **What do you dislike about Amazon SageMaker?** Though we are getting compute for a reasonable costs, the onus of responsibility to run the large model lies witth the users. When they run larger models just to test it is attracting some additional costs. Though Sagemaker is easy to use, the cost management responsibility lies with the users. **What problems is Amazon SageMaker solving and how is that benefiting you?** Data Labeling and Preparation, notebooks for Development ### 19. Amazon SageMaker review **Rating:** 5.0/5.0 stars **Reviewed by:** Gourav J. | Machine Learning Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 10, 2023 **What do you like best about Amazon SageMaker?** I am exclusively using Amazon SageMaker for both professional and personal usage. The variety of application make Handy while work upon machine learning task. The training and canvas features i've been using for quite some time there application make my ML task faster and productive **What do you dislike about Amazon SageMaker?** Amazon SageMaker is great platform for Ml task all features and applications are really easy to use. The feature I need attention is the the offered free trial which are not sufficient and Amazon should provide gpu access also. Apart from this it's a great ml online platform **What problems is Amazon SageMaker solving and how is that benefiting you?** I am a data scientist my most of the work are based of ML Modal research and development. The tool which provides by SageMaker is really helpful for me on daily task such as training infences and deployment ### 20. Not great with image input model **Rating:** 3.0/5.0 stars **Reviewed by:** Femina B. | Freelancer, Small-Business (50 or fewer emp.) **Reviewed Date:** June 07, 2023 **What do you like best about Amazon SageMaker?** i like how wonderfully it works based on numbers data or text data. i tried working on it along with other aws products like aws lamda and aws api gateway. and the documents or examples are also good for it **What do you dislike about Amazon SageMaker?** i wanted to work on passing and image. or video input and get the image output out of it but was not really helpful as it takes the data in excel format and then we have to save it in s3 bucket. it retrives he data from it but not sure how to pass on in image in it. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon is great with data science projects like cosine similarity and other similar projects as far as i worked on it and it has benefited me also the sample projects attached in it is great. would defenitely work on it in future also we get access to change the gpu whenever we need so thats another great service. ### 21. Complete AWS based AI ML Studio **Rating:** 4.0/5.0 stars **Reviewed by:** Avineet A. | Sr. Cloud Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** October 10, 2023 **What do you like best about Amazon SageMaker?** Ability to implement AI ML capabilities and leverage existing ML models. Ability to integrate CI CD pipelines for MLOps. **What do you dislike about Amazon SageMaker?** User Interface could be less cluttered and controlled, needs to be more web like. At the moment it looks and feels like a client tool hosted on web. CI CD can be more self managed. **What problems is Amazon SageMaker solving and how is that benefiting you?** Implement custom and out of the box AI and ML solutions from AWS and make it cloud based solution which enables quick collaboration and great foundation models that are supported. ### 22. Amazon Sagemaker Benefits **Rating:** 5.0/5.0 stars **Reviewed by:** Shyam P. | Engineer - Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 13, 2023 **What do you like best about Amazon SageMaker?** I like the endpoint creation which can inference our model through lambda function. Along with Sagemaker I used API gateway as well as to use the model in local environment. **What do you dislike about Amazon SageMaker?** When I used sagemaker for object detection using py torch then it was not taking the image and I used to resize the image and then pass it to model. But in local it was working without even resizing the image **What problems is Amazon SageMaker solving and how is that benefiting you?** It can build the model and can be infered after endpoint creation and after that further processing can be done via lambda function and API gateway. ### 23. AWS SageMaker for training/deployment of large-scale recommenders **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** June 14, 2023 **What do you like best about Amazon SageMaker?** SageMaker makes it very easy to train and deploy models. The managed infrastructure allows us to focus on business logic without needing to deal with things like cluster management, autoscaling, etc. **What do you dislike about Amazon SageMaker?** Sometimes things can be a little overcomplicated to use. For example, the batch transform functionality requires us to write separate inference containers in addition to real-time inference containers. **What problems is Amazon SageMaker solving and how is that benefiting you?** AWS SageMaker helps us build and deploy large-scale recommenders in both offline batch and online ecosystems. We are much more productive because of AWS SageMaker ### 24. Lots of features for ML Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** April 25, 2023 **What do you like best about Amazon SageMaker?** Sagemaker provides a platform for executing ML workflows well-integrated with other AWS services such as S3, Secrets Manager and Lambda etc. **What do you dislike about Amazon SageMaker?** The Sagemaker Pipelines UI is less friendly than other pipeline orchestration tools such as Airflow and Azure. Sagemaker Studio and Image terminals are nowhere as nice as specialized IDEs such as IntelliJ for development purposes **What problems is Amazon SageMaker solving and how is that benefiting you?** Most of our ML workflows are orchestrated and scheduled within Sagemaker. ### 25. I use it often for Data Science Solutions **Rating:** 4.0/5.0 stars **Reviewed by:** Debjyoti S. | Lead Data Scientist, Consulting, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2023 **What do you like best about Amazon SageMaker?** It's really helps in deploying applications faster and in a very convenient way. The pricing is also light on pocket. **What do you dislike about Amazon SageMaker?** The thing I dislike about Amazon Sagemaker is, we need to remember some of the commands. **What problems is Amazon SageMaker solving and how is that benefiting you?** Deployment is much easier ### 26. Sagemaker for image based applications **Rating:** 4.5/5.0 stars **Reviewed by:** Imrankhan A. | Senior software engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** June 14, 2023 **What do you like best about Amazon SageMaker?** In sagemaker we can have some of basic models and we can create complex AI models and train and test without hassle. **What do you dislike about Amazon SageMaker?** Sagemaker is not having extensive data for images. **What problems is Amazon SageMaker solving and how is that benefiting you?** Forecasting of future emission levels ### 27. Reliability and Reputation **Rating:** 5.0/5.0 stars **Reviewed by:** Akpovi Ludovic A. | General Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** December 08, 2022 **What do you like best about Amazon SageMaker?** Amazon SageMaker is widely present all over the world with remarkable technical support. Excellent product knowledge and details, and the best shipment tracking. **What do you dislike about Amazon SageMaker?** Destination restrictions for certain products for reasons I do not know. But these warnings are instructive to avoid losing money. **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker allows me to chat in real-time and get advice on products and flexible payment methods with Gift Cards and bank transfers. Speed, efficiency, and reliability of transactions. ### 28. Can improve the deployment process **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** June 14, 2023 **What do you like best about Amazon SageMaker?** Unlike GCP its little better to use in my point of view. **What do you dislike about Amazon SageMaker?** When its coming to own algorithms deployment its little hard comparing to azure. **What problems is Amazon SageMaker solving and how is that benefiting you?** Machine learning model creation ### 29. Utilizing AWS instances for its compute **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Enterprise (> 1000 emp.) **Reviewed Date:** May 06, 2023 **What do you like best about Amazon SageMaker?** Has GPU options instances to choose from **What do you dislike about Amazon SageMaker?** Takes a long time to spin up plus setting up a virtual environment within sagemaker is almost impossible **What problems is Amazon SageMaker solving and how is that benefiting you?** It is allowing for a larger computation power and instances to train machine learning models ### 30. Easier ML models training, testing and deployment **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** June 21, 2022 **What do you like best about Amazon SageMaker?** It offers a lot of prettained deep learning and ML models that reduces the time of small projects drastically. It's easy to work with for the first time and doesn't require prior knowledge. It's the best for deploying mosel with simplicity. **What do you dislike about Amazon SageMaker?** It's quite expensive indeed and especially for large projects that will need more running time. It doesn't leave much room for customization as it has a set workflow with not much flexibility. **What problems is Amazon SageMaker solving and how is that benefiting you?** It's helping make ML faster, easier and more accessible and also aids with model deployment. I used it for multiple Object Detection ML projects as well as for model deployment. ### 31. Amazon Sagemaker is very user friendly **Rating:** 5.0/5.0 stars **Reviewed by:** Manju S. | Data Labeling Specialist, Enterprise (> 1000 emp.) **Reviewed Date:** April 21, 2022 **What do you like best about Amazon SageMaker?** Sagemaker was very easy to use and the interface was user friendly **What do you dislike about Amazon SageMaker?** There was no access for the user to check her daily stats. The access was restricted. **What problems is Amazon SageMaker solving and how is that benefiting you?** Traning the Airbnb bot to answer the users and save customer service time and efforts ### 32. SageMaker for Machine Learning **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** November 04, 2021 **What do you like best about Amazon SageMaker?** Amazon Sagemaker is a managed service for data science, We like SageMaker because it supports a lot of in-built algorithms, we especially use liner learner algorithms for solving predictive maintenance use cases **What do you dislike about Amazon SageMaker?** We noticed, as a startup, when we are beginners. If we failed to stop unused sage make endpoint.That will end up in huge bills. Maybe looking for a better solution to fix this issue. **What problems is Amazon SageMaker solving and how is that benefiting you?** we especially use liner learner algorithms for solving predictive maintenance use cases & we are also using Sagemaker Neo for compiling Sagemaker trined Ml modles. ### 33. AWS Sagemaker for easy deployment **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** June 11, 2020 **What do you like best about Amazon SageMaker?** We can quickly and easily build, train, and deploy machine learning models at any scale. **What do you dislike about Amazon SageMaker?** All features are good is Amazon Sagemaker machine learning **Recommendations to others considering Amazon SageMaker:** every data scientist has to use sagmaker **What problems is Amazon SageMaker solving and how is that benefiting you?** We can quickly build, train, and deploy machine learning models. One important thing is we can models scale, i.e., suppose models require more RAM/memory or taking too much time to run; we can quickly increase memory. All models will be there in the cloud. ### 34. One of the best tool to deloy machine models **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** October 06, 2021 **What do you like best about Amazon SageMaker?** AWS sagemaker is one the best service that can help in enabling data scientists and facilitates developers to quickly and easily build, train, and deploy machine learning models at any scale at anytime. **What do you dislike about Amazon SageMaker?** The service is not easy to learn. You gotta get yourself trained and certified first.. **What problems is Amazon SageMaker solving and how is that benefiting you?** Helping people to adopt change and deloy machine learning models on different parameters. ### 35. Excellent service for ML end to end journey for small team **Rating:** 4.0/5.0 stars **Reviewed by:** Vineet J. | Data Science Manager, Enterprise (> 1000 emp.) **Reviewed Date:** June 19, 2020 **What do you like best about Amazon SageMaker?** 1. Auto-scaling of Infra 2. Out of the box versioning of model and training 3. Great integration with AWS eco-system 4. Easy monitoring and debugging 5. Custom algorithm support with out of the box algorithm 6. Multi Model Server support 7. Great support for small team where we don't have infra expert and only need to focus on Machine Learning 8. Great support on AWS CLI **What do you dislike about Amazon SageMaker?** 1. Very bad documentation 2. Only AWS components are used like S3 for data, ECR for Docker etc 3. Very high cost for endpoints even they are not in used 5. Not good for team where we have infra experts, giving less control for underlying infra **Recommendations to others considering Amazon SageMaker:** It's really very good if you only want to focus on Machine Learning part, not on Infra. **What problems is Amazon SageMaker solving and how is that benefiting you?** Working with Sagemaker since last 2 years, used it for multi real time recommendation system and content intelligence ### 36. AWS SageMaker **Rating:** 5.0/5.0 stars **Reviewed by:** Nilden T. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 15, 2021 **What do you like best about Amazon SageMaker?** Hyperparameter optimization, integration with EC2, Forecasting and Personalized services and AutoPilot **What do you dislike about Amazon SageMaker?** Deployment phase of a data science model **What problems is Amazon SageMaker solving and how is that benefiting you?** Advanced notebook usage, automatic hyperparameter optimization services ### 37. Amazon Sagemaker: Everything at one place for data scientist if you spent time to learn it **Rating:** 4.0/5.0 stars **Reviewed by:** Ramavtar M. | Senior Software Engineer - ML, Enterprise (> 1000 emp.) **Reviewed Date:** June 19, 2020 **What do you like best about Amazon SageMaker?** 1) The flexibility to deploy your own algorithms using Docker which is missing in other big cloud ML platforms such as GCP AI. (This is very useful for its integration to other existing ML tools such as MLflow) 2) Support for A/B testing of ML Models 3) Support for batch processing 4) Support for lambda scheduling 5) A very well managed GitHub sagemaker repository supported by blogs to provide all necessary examples to get started. 6) Support for Reinforcement learning. 7) Support for third party software through AWS market place. **What do you dislike about Amazon SageMaker?** 1) The very unclear documentation/support for Models monitoring after deployment over Sagemaker endpoints. 2) Too many APIs to do the same thing. Which might confuse the user in finding which one is the best way. 3) UI can be better. 4) Project Management and adding collaborators feature is missing or not directly supported. 6) The logs tracking through Cloudwatch can be better managed. 7) It should provide an out-of-the-box workflow management solution such as Airflow. **Recommendations to others considering Amazon SageMaker:** I found Amazon Sagemaker better than GCP AI and IBM Watson for my use case. It was mainly related to having support for custom algorithms. Regarding the existing algorithm such as Linear regression, XGBoost, and Deep Learning all available solutions are more or less comparable. Also, Support for A/B testing of ML Models can be a very useful consideration **What problems is Amazon SageMaker solving and how is that benefiting you?** Solved problems of Deployment of ML models. The inbuilt support for Autoscaling and A/B testing proved beneficial for the use cases. ### 38. Training to Deployment : All in one place **Rating:** 4.0/5.0 stars **Reviewed by:** Judy T. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 18, 2020 **What do you like best about Amazon SageMaker?** Sagemaker lets you build, train and deploy your models all in one place. I like how it lets you choose different machine types for each phase of development so no resources are wasted. Sagemaker also supports distributed training. I've used Sagemaker to build, train and deploy deep learning models using PyTorch, Tensorflow as well as Keras. It provides lots of custom conda environments to support development using any framework, like p36, p32 etc. Sagemaker instances comes preloaded with some sample notebooks. There's also a large repository of materials available to help you get started. **What do you dislike about Amazon SageMaker?** It's bit pricey and the UI doesn't exactly tell you if you've go unused instances or deployed models lying around unlike the GCP equivalent. Amazon sneaks up on you with unexpected bills if you aren't careful. It's a bit difficult to get started with as the whole user creation vs instance creation kinda trips you up. As soon as you click on preview, amazon directs you to the user creation part and you've a whole lot of unneccesary set up when all you probably need is to create a single notebook instance. **Recommendations to others considering Amazon SageMaker:** Watch out for the bill. Don't leave instances running over night. You'll wake up to a nasty surprise. **What problems is Amazon SageMaker solving and how is that benefiting you?** I've been using Sagemaker for building PoCs and it's perfect for my use case. I can build, train and deploy my models all in one place using the optimum instance types. Sagemaker also provides out-of-the-box support for a lot of ml algorithms so it's plug and play sometimes. ### 39. Excellent product for end to end Machine Learning **Rating:** 4.5/5.0 stars **Reviewed by:** Vaibhav S. | Enterprise (> 1000 emp.) **Reviewed Date:** June 22, 2020 **What do you like best about Amazon SageMaker?** Easy deployment of ML Model as HTTP endpoints. **What do you dislike about Amazon SageMaker?** Not enough documentation available on latest features like Batch Transform **Recommendations to others considering Amazon SageMaker:** One of the best tool for end to end Machine Learning **What problems is Amazon SageMaker solving and how is that benefiting you?** Using Sagemaker to build xgboost model. Its UI is very well structured and has support for almost every ML model and python packages. ### 40. Best AutoML tool available in current era **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Mid-Market (51-1000 emp.) **Reviewed Date:** September 30, 2019 **What do you like best about Amazon SageMaker?** you can Integration with other AWS ecosystem with very easily, one of the best features of Amazon SageMaker. Plus you will get charged based on your usage. it has jupyter notebook which facilitates developer efficiency and it is also very scalable and customization is also available at the fingertip. All Machine learning algorithms are very well defined and understandable and any machine learning engineer with naive experience can make it possible to work with Sagemaker. Most of the machine learning, as well as deep learning libraries, are supported. **What do you dislike about Amazon SageMaker?** It demands good coding knowledge to use it. if you dint have any prior experience of coding or coding language you might feel hard to use it. features like drag and drop are missing which are available with other tools. on a larger dataset sometime it takes a longer time than expected. **Recommendations to others considering Amazon SageMaker:** I would advise to those people who have good knowledge of coding and want to develop autoML and customize it. Every ML engineer must try it once **What problems is Amazon SageMaker solving and how is that benefiting you?** We are using Amazon SageMaker to build models on healthcare data. by doing this we can have a proper solution for our problems so we can go deep in the process of analyzing data and finalizing approach. we are using possible amazon ecosystem tool to facilitates our work and make it done within a predefined time. ### 41. Sage maker is an excellent tool, we can easily do our ML models **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Mid-Market (51-1000 emp.) **Reviewed Date:** June 11, 2020 **What do you like best about Amazon SageMaker?** Best one is this is online machine learning platform very easy to understand and train developers, it is so easy to implement all our machine learning models, one can easily do with out much expertise **What do you dislike about Amazon SageMaker?** I believe all good, may be some sort of UI experience need to improve. All are good only **Recommendations to others considering Amazon SageMaker:** Keep it up keep going, we like the product **What problems is Amazon SageMaker solving and how is that benefiting you?** We use sage maker to run algorithms and do ML models for business analysis, it can help predict the customer behavior ### 42. Sagemaker is a great ML tool to use in business world **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Research | Small-Business (50 or fewer emp.) **Reviewed Date:** June 19, 2020 **What do you like best about Amazon SageMaker?** The overall simplicity it provides as a tool. **What do you dislike about Amazon SageMaker?** Truth is i haven't found anything negative for now **Recommendations to others considering Amazon SageMaker:** If you decide to use Sagemaker to bring ml proucts into production, you will really get excited about the ease of use. **What problems is Amazon SageMaker solving and how is that benefiting you?** We deploy ml models in AWS using SageMaker. The easy deployment steps probably make SageMaker such a popular choice. ### 43. Sagemaker is useful for end to end ML pipeline **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** June 10, 2020 **What do you like best about Amazon SageMaker?** I like the concept of having an infrastructure where you can train the model and deploy to the public for seamlessly. **What do you dislike about Amazon SageMaker?** I feel there could be more helpful resources available for the getting started that would help. **What problems is Amazon SageMaker solving and how is that benefiting you?** I am not facing any problems just I want to learn more how can cloud watch logging for the logs happens. ### 44. Its alive **Rating:** 1.0/5.0 stars **Reviewed by:** Verified User in Information Services | Small-Business (50 or fewer emp.) **Reviewed Date:** June 18, 2020 **What do you like best about Amazon SageMaker?** I like the AWS environment and automated access to Jupyter notebooks. **What do you dislike about Amazon SageMaker?** It restricts the way I am willing to do my work. **What problems is Amazon SageMaker solving and how is that benefiting you?** Mainly training new models. ### 45. Easy to train and deploy the Machine Learning model **Rating:** 4.0/5.0 stars **Reviewed by:** Vivek A. | Web Application Developer, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** September 05, 2018 **What do you like best about Amazon SageMaker?** I have worked with DeepLens by AWS for various face recognition application. It's very easy to use and configure. You don't need your own NVIDIA machine to train the model. If you have more data and need a powerful machine to train. Just upgrade your cloud and start training. **What do you dislike about Amazon SageMaker?** Sometimes AWS doesn't say some of the configurations explicitly, you have to be familiar with AWS. There are very less good resources for that. Our company has some help from AWS marketing developer **What problems is Amazon SageMaker solving and how is that benefiting you?** We are trying to solve the problem of Student paying attention in the class or not and if not what are the reasons behind that. If we are able to solve the problem, we can increase student's interest in the subject which directly helps student in his career and life. ### 46. Automatic Learning ! **Rating:** 5.0/5.0 stars **Reviewed by:** maría jose g. | Software Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** June 22, 2018 **What do you like best about Amazon SageMaker?** I love this platform is so simple because it allows me as a developer to create data in a simple way I do not have to work in a tedious way implementing algorithms because it gives me everything is so simple it saves me many hours of work because with it I can implement up to my own framework I work without any problem, I can also have notebooks and connect directly in S3 . To be able to verify if the model I have generated I like I can do integrated A / B tests and in this way I achieve better results and success in what I look for . its simplicity has allowed me to work very efficiently and very quickly acquiring very complete and successful works and models **What do you dislike about Amazon SageMaker?** for beginners who wish to use this platform the only disadvantage is that it requires very high payments because this platform implements the GPU computation to provide a better training of the GPU which makes them faster **Recommendations to others considering Amazon SageMaker:** it is a great platform I recommend it, but at the beginning it generates a lot of expenses you can recover as you implement it in your business **What problems is Amazon SageMaker solving and how is that benefiting you?** Amazon SageMaker gives me easier access to other known learning frameworks, while managing the infrastructure for the creation, training and delivery of my work models, since I can evaluate and implement models in a flexible and efficient way in a very simple way ### 47. ML/AI with AWS SageMaker **Rating:** 4.5/5.0 stars **Reviewed by:** Alvaro I. | Director of Web Development, Mid-Market (51-1000 emp.) **Reviewed Date:** June 20, 2018 **What do you like best about Amazon SageMaker?** - Lots of Amazon examples of use cases where you will use this kind of tools - The configuration for your environment is super easy - The SDK is simple to use **What do you dislike about Amazon SageMaker?** - Since it's "new," you won't find much about it on the Internet. - I think it would be super helpful to have other languages in the Development Kit besides Sparks and Python (probably Ruby or Java or even JavaScript). **Recommendations to others considering Amazon SageMaker:** Learn Python **What problems is Amazon SageMaker solving and how is that benefiting you?** At this stage, we are testing it with a parking rotation inside our business offices. It's more than a Proof of Concept kind of app, which has helped me fully understand the concepts and the utility of this tool. If you are planning to start digging into the Machine Learning and Artificial Intelligence world, this might be the best way to start. You will obviously need to understand how to code, but if you are familiar with Python, that will be enough. ### 48. My experience has been good with this software. **Rating:** 4.0/5.0 stars **Reviewed by:** Saransh D. | Graduate Research Assistant, Higher Education, Enterprise (> 1000 emp.) **Reviewed Date:** June 29, 2018 **What do you like best about Amazon SageMaker?** It has enough room for experienced people as well as for those who just need things done without going much deeper into building models. It provides easy and efficient customization which easy to alter and change. If one is already using Amazon, then there is no need to transition. There are a lot of examples available to work with. **What do you dislike about Amazon SageMaker?** The algorithms are there and they can be implemented with ease but the user needs better description as to where they are located so that they can find them more easily. There should be some options which make this mobile friendly. Since it is a new software, there isn't much documentation out for this software. **What problems is Amazon SageMaker solving and how is that benefiting you?** It helps me to understand how different learning frameworks wok and helps me in better implementation of my learning algorithms. ### 49. best one if you are in the eco system **Rating:** 4.5/5.0 stars **Reviewed by:** Anil Sai B. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** October 06, 2018 **What do you like best about Amazon SageMaker?** I love AWS services and this one is no different. We are very much in the AWS world so deploying models using Sagemaker is seamless. The ease of building and training models is definitely a plus. **What do you dislike about Amazon SageMaker?** Lack of huge library of trained models like tensor flow yet but I am sure it will get there. **What problems is Amazon SageMaker solving and how is that benefiting you?** We using it to deploy models in the data pipeline for data predictions. ### 50. Remove the barries to machine learning **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Internet | Small-Business (50 or fewer emp.) **Reviewed Date:** August 22, 2018 **What do you like best about Amazon SageMaker?** it has a lot of tools to help with training, and deploying ML models. They manage all the training techniques and the tunning of the models. So you can focus on getting something in production faster. They support many different algorithms and there is always the option to use a personal preference using docker. **What do you dislike about Amazon SageMaker?** Not much information about it online and the prices can prevent developers from using it. **Recommendations to others considering Amazon SageMaker:** If you are a company think on how much are you expecting of making after using this technology. It can become expensive quickly unless you know what you are doing. **What problems is Amazon SageMaker solving and how is that benefiting you?** This is pretty much new for us. So it is basically testing and checking the things that can be done using this technology. ## Amazon SageMaker Discussions - [What is the best way to integrate Sagemaker models with Kubernetes?](https://www.g2.com/discussions/28784-what-is-the-best-way-to-integrate-sagemaker-models-with-kubernetes) - 1 comment - [How do i make this platform reach to most of my developers?](https://www.g2.com/discussions/27976-how-do-i-make-this-platform-reach-to-most-of-my-developers) - 1 comment - [View Amazon SageMaker pricing details and edition comparison](https://www.g2.com/products/amazon-sagemaker/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-26+00%3A42%3A20+-0500&secure%5Bsession_id%5D=5c666edf-de4c-4c03-8cf3-7d6c0f5a0910&secure%5Btoken%5D=251a43cfe10b2ef3dbb80027b87b1984649713c52558c60b7994d6e97c05ad01&format=llm_user) ## Amazon SageMaker Integrations - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews) - [AWS Amplify](https://www.g2.com/products/aws-amplify/reviews) - [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - [GitLab](https://www.g2.com/products/gitlab/reviews) ## Amazon SageMaker Features **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Data Ingestion & Preparation - Low-Code Machine Learning Platforms** - Automatic Data Profiling & Quality Assessment - Multi‑Source Connector Support - Schema Drift / Change Detection **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Operations** - Metrics - Infrastructure management - Collaboration **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Model Construction & Automation - Low-Code Machine Learning Platforms** - Guided Algorithm & Hyperparameter Recommendation - Code Extensibility - Automated Feature Engineering **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Management** - Cataloging - Monitoring - Governing **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Deployment** - Managed Service - Application - Scalability **Generative AI** - AI Text Generation - AI Text Summarization **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making ## Top Amazon SageMaker Alternatives - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - 4.3/5.0 (650 reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) - 4.4/5.0 (187 reviews) - [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) - 4.3/5.0 (87 reviews)