Medium
kadiyala.medium.com › aws-sagemaker-use-cases-pros-and-cons-da68b8afd30a
AWS Sagemaker Use Cases, Pros, and Cons | by Srinivasa Kadiyala | Medium
December 16, 2023 - Integration with AWS Ecosystem: SageMaker integrates with other AWS services, such as S3 for data storage, IAM for access control, and CloudWatch for monitoring, providing a comprehensive ecosystem for machine learning workflows. ... Cost: While SageMaker provides cost-effective solutions, running large-scale training jobs or deploying models for high-traffic inference can increase costs. Users should carefully manage resources to optimize costs.
ProjectPro
projectpro.io › recipes › explain-use-cases-of-aws-sagemaker
Explain the use cases of the AWS SageMaker -
April 10, 2023 - Amazon SageMaker is optimized for various popular deep learning frameworks such as TensorFlow, Apache MXNet, PyTorch, and more. Frameworks are always up-to-date with the latest version and are optimized for performance on AWS. Users don’t need to manually set up these frameworks and further can use them within the built-in containers.
What are your opinions on machine learning platforms like AWS SageMaker and Azure Machine Learning?
We’ve been using AWS SageMaker at my company for the past year and we’ve essentially delegated it to a convenient way to run Jupyter Notebooks within our AWS environment. If you need to do anything beyond simply fitting a single model, you will spend hours trying to hack your use case into ... More on reddit.com
Machine learning for all developers with edX and Amazon SageMaker | Amazon Web Services
I kind of don't like the way a lot of the material around SageMaker is marketed. I really think it's misleading, although maybe not intentionally so.
Most of the material talks about something to effect of "machine learning for developers." I disagree with that statement. Yes, SageMaker allows for easier creation, training, and deployment of machine learning models. However, it conveys none of the subject matter expertise around machine learning. When I see this, I just get worried about the proliferation of bad models with fundamental problems like data leakage. After all, you pick up the tensorflow API and that's all there is to this ML stuff, right?!
The better way to market this product in terms of where it fits in the industry is that Amazon provides the data platform so that your data scientists don't also have to be engineers (DevOps or ML). That fits a much better market need. However, that's also a smaller potential market, so I can understand why they're riding the hype train of software engineers wanting to dabble with ML.
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September 24, 2016AWS Sagemaker use case
Uhhhh... If you will never, ever be able to know what the syntax of those variables is, maybe one of the Bedrock models might be a good bet? Have it spit out a JSON of what it thinks the variables are? GPT models are pretty good at text analysis. Otherwise, good ol' regex if you already know what they are going to look like. Both Bedrock and SageMaker are going to be expensive. More on reddit.com
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December 16, 2023Any machine learning novices use SageMaker to do something useful?
It's mostly for research still, in my eyes. If you want to play, first find a public dataset, or maybe some large amount of data you already have; this will be your training data. Then choose a column from your data that you would like to predict the value of in relation to the other columns in each row. You have just come up with an idea for your own ML model that you could build with TensorFlow in Sagemaker.
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December 4, 2014How much does SageMaker charge?
SageMaker charges you for select AWS services like ML compute, processing resources, and storage that you utilize throughout the process. The total depends on the number and type of instances you select. You can check all the services enlisted in the service catalog and refer to the AWS SageMaker Studio pricing page for further details.
projectpro.io
projectpro.io › blog › 10 amazon sagemaker project ideas and examples for practice
10 Amazon SageMaker Project Ideas and Examples for Practice
Is SageMaker just Jupyter?
SageMaker was developed based on Jupyter, and its interface is an extension of the JupyterLab interface. However, it is different from Jupyter in that it is a fully managed ML Amazon Elastic Compute Cloud compute instance that runs on Jupyter notebooks-like structures.
projectpro.io
projectpro.io › blog › 10 amazon sagemaker project ideas and examples for practice
10 Amazon SageMaker Project Ideas and Examples for Practice
Videos
Build Generative AI with Amazon SageMaker - AWS
SageMaker for High-Performance ML Applications - AWS
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Use Amazon SageMaker to Build Generative AI Applications - AWS ...
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5 AWS SageMaker Algorithms & Their Use Cases - YouTube
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Built-in Machine Learning Algorithms with Amazon SageMaker - a ...
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Fully-Managed Notebook Instances with Amazon SageMaker - a Deep ...
TrustRadius
trustradius.com › home › ai development platforms › amazon sagemaker
Use Cases of Amazon SageMaker 2025
Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › data labeling with a human-in-the-loop › using amazon augmented ai for human review › use cases and examples using amazon a2i
Use Cases and Examples Using Amazon A2I - Amazon SageMaker AI
You can use Amazon Augmented AI to incorporate a human review into your workflow for built-in task types , Amazon Textract and Amazon Rekognition, or your own custom tasks using a custom task type .
Amazon Web Services
pages.awscloud.com › Amazon-SageMaker-for-Media-and-Entertainment-Use-Cases_2021_0719-MCL_OD.html
Amazon SageMaker for Media and Entertainment Use Cases | AWS Online Tech Talks
From video and music streaming to internet advertising and sports, Amazon SageMaker helps customers make smarter content investments, better monetize content libraries, and delight users with personalized experiences.
Medium
chirayushah7.medium.com › amazon-sagemaker-an-overview-of-amazon-sagemakers-features-use-cases-and-benefits-a4a029fc132c
Amazon SageMaker: An overview of Amazon SageMaker’s features, use cases, and benefits. | by Chirayu shah | Medium
April 8, 2023 - Integration with Other AWS Services: To offer a full machine learning solution, SageMaker may be linked with other AWS services like Amazon S3, AWS Lambda, and AWS Glue. ... Reduced Time to Market: With SageMaker, data scientists and developers can easily and quickly create, train, and deploy machine learning models, which cuts down on the time it takes for new goods and services to hit the market. SageMaker’s ability to scale up or down to handle shifting workloads enables firms to only pay for the resources they actually use.
IBM
ibm.com › think › topics › amazon-sagemaker
What Is Amazon SageMaker? | IBM
November 17, 2025 - After deploying machine learning models, SageMaker allows for both real-time and batch inference. Users can create endpoints—specific URLs that serve as access points for applications—to make real-time predictions and manage workloads efficiently. This is particularly useful for applications requiring instant responses, such as in generative AI scenarios. With features like auto scaling and integration with AWS Lambda, SageMaker provides serverless capabilities that help manage computing resources dynamically based on demand.
ProjectPro
projectpro.io › blog › 10 amazon sagemaker project ideas and examples for practice
10 Amazon SageMaker Project Ideas and Examples for Practice
October 28, 2024 - It caters to different types of ... platform can be used for several use cases like access control, number plate recognition, recommendation system, business analytics with NLP, etc....
AWS
aws.amazon.com › blogs › machine-learning › category › case-study
Case Study | Artificial Intelligence
The team uses Amazon Nova for intelligent narrative generation to provide parents with meaningful insights into their children’s gaming activities and social interactions, while maintaining a non-intrusive approach to monitoring. In this post, we explore Clearwater Analytics’ foray into generative AI, how they’ve architected their solution with Amazon SageMaker, and dive deep into how Clearwater Analytics is using LLMs to take advantage of more than 18 years of experience within the investment management domain while optimizing model cost and performance.
Simplilearn
simplilearn.com › home › resources › cloud computing › aws tutorial: a step-by-step tutorial for beginners › an introduction to aws sagemaker
An Introduction to AWS SageMaker
January 30, 2025 - Learn what is AWS SageMaker✔️, advantages of sagemaker, and how to use machine learning with AWS sagemaker. Read to learn more about sagemaker
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Applify
applify.co › blog › aws-sagemaker
Empowering machine learning solutions with AWS SageMaker | Applify BlogEmpowering machine learning solutions with AWS SageMaker
AWS SageMaker is revolutionizing ... care. For instance, healthcare providers can use SageMaker to analyze patient data and predict outcomes, leading to improved treatment plans and resource allocation....
AWS
aws.amazon.com › blogs › big-data › an-integrated-experience-for-all-your-data-and-ai-with-amazon-sagemaker-unified-studio
An integrated experience for all your data and AI with Amazon SageMaker Unified Studio | AWS Big Data Blog
March 14, 2025 - In the following sections, we provide a few example use cases. SageMaker Unified Studio provides a unified JupyterLab experience across different languages, including SQL, PySpark, and Scala Spark. It also supports unified access across different compute runtimes such as Amazon Redshift and Amazon Athena for SQL, Amazon EMR Serverless, Amazon EMR on EC2, and AWS Glue for Spark.
Medium
aws.plainenglish.io › 86-aws-everything-you-need-to-know-about-aws-sagemaker-for-aws-ai-practitioner-aif-c10-exam-458bfc9c99df
86)AWS-Everything You need to know about AWS SageMaker For AWS AI Practitioner (AIF-C10) Exam | by Venkatramanan C S | AWS in Plain English
September 24, 2024 - Amazon SageMaker is a managed service in the Amazon Web Services (AWS) public cloud. It provides the tools to build, train and deploy machine learning (ML) models for predictive analytics applications. Amazon SageMaker helps data scientists and ML engineers build FMs from scratch, evaluate ...
Softweb Solutions
softwebsolutions.com › resources › benefits-of-amazon-sagemaker
Top 10 Benefits of Amazon SageMaker for Machine Learning
February 28, 2023 - Feature reduction is mostly used for quantitative finance, image compression, facial recognition. SageMaker can be easily integrated with other AWS services, providing a seamless and integrated experience, making it easy to build complete machine ...
Saturn Cloud
saturncloud.io › blog › a-detailed-guide-to-amazon-sagemaker
A Detailed Guide to Amazon SageMaker | Saturn Cloud Blog
March 12, 2024 - After training, the resulting model artifacts are stored in Amazon S3, which you can use for inference or further analysis. SageMaker Training seamlessly integrates with other AWS services, enhancing its capabilities and making it a versatile tool for machine learning tasks.
infiniticube -
infiniticube.com › home › how aws sagemaker ensures reliable impact on machine learning developers and data scientists?
Sagemaker AWS: Features, Applications and Automation Tools
July 2, 2025 - Custom-built algorithms written in one of the supported ML frameworks or any code packaged as a Docker container image can also be used by developers. SageMaker can retrieve data from Amazon Simple Storage Service (S3), and the data set has no practical limit in terms of size. A developer begins by logging into the AWS SageMaker console and launching a notebook instance.
Amazon Web Services
aws.amazon.com › products › analytics › amazon sagemaker
The center for all your data, analytics, and AI – Amazon SageMaker – AWS
5 days ago - Unify data access across Amazon S3 data lakes, Amazon Redshift data warehouses, and third-party and federated data sources with a lakehouse architecture in Amazon SageMaker ... Bringing together widely adopted AWS machine learning (ML) and analytics capabilities, the next generation of Amazon SageMaker delivers an integrated experience for analytics and AI with unified access to all your data.
GitHub
github.com › aws › amazon-sagemaker-examples
GitHub - aws/amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. - GitHub - aws/amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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GeeksforGeeks
geeksforgeeks.org › machine learning › what-is-sagemaker-in-aws
What is SageMaker in AWS? - GeeksforGeeks
1 week ago - Unlike traditional ML development, which requires managing complex infrastructure for training and hosting, SageMaker abstracts away the heavy lifting. It provides a unified toolset for every step of the ML lifecycle, from labeling raw data to monitoring production models for drift. SageMaker is built around three distinct stages of the ML lifecycle. Importantly, you can use these independently.
Amazon Web Services
aws.amazon.com › analytics › amazon sagemaker › lakehouse architecture
Amazon SageMaker - Unified, Open, and Secure Data Lakehouse Architecture
5 days ago - “We have been using Amazon Redshift to gain insights from both structured and semistructured data across all our data repositories. The new Amazon SageMaker Lakehouse excites me with its potential to enhance and unify access to data lakes or other data sources with services like Amazon Redshift, AWS Glue Data Catalog, and AWS Lake Formation.