AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › what is amazon sagemaker ai?
What is Amazon SageMaker AI? - Amazon SageMaker AI
Amazon SageMaker AI is a fully managed machine learning (ML) service. With SageMaker AI, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment.
Amazon Web Services
aws.amazon.com › products › analytics › amazon sagemaker
The center for all your data, analytics, and AI – Amazon SageMaker – AWS
1 week ago - Build, train, and deploy ML and FMs with fully managed infrastructure, tools, and workflows with Amazon SageMaker AI ... Analyze, prepare, and integrate data for analytics and AI using open source frameworks on Amazon Athena, Amazon EMR, and AWS Glue
Guide for Sagemaker AI.
You can't do any serious AI training for free or minimal to no expense on AWS or any other Cloud provider. It is very heavy on GPU compute and people don't give that away for free. I would recommend deep diving AI training to understand what you're asking about. Sage Maker has some free labs you can play with https://studiolab.sagemaker.aws/ More on reddit.com
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May 24, 2025SageMaker is Terrible - Is Using EC2 a Better Alternative?
The biggest and best companies are all using EKS for ML training and inferencing either with MLflow or their own custom pipelines. Sagemaker is bloated and expensive. More on reddit.com
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March 7, 2025SageMaker costs for AI model
This recommendation was generated using AWS Generative AI capabilities. You are responsible for evaluating the recommendation in your specific context and implementing appropriate oversight and safeguards. Learn more · Thank you for providing details about your SageMaker deployment. More on repost.aws
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March 18, 2025Why not Sagemaker?
Con: Bloody expensive More on reddit.com
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April 5, 2023Videos
Amazon UK Press Centre
press.aboutamazon.com › 2024 › 12 › new-amazon-sagemaker-ai-innovations-reimagine-how-customers-build-and-scale-generative-ai-and-machine-learning-models
New-Amazon-SageMaker-AI-Innovations-Reimagine-How-Customers-Build-and-Scale-Generative-AI-and-Machine-Learning-Models - US Press Center
December 10, 2024 - SageMaker customers can now easily and securely discover, deploy, and use fully managed generative AI and machine learning (ML) development applications from AWS partners, such as Comet, Deepchecks, Fiddler AI, and Lakera, directly in SageMaker, giving them the flexibility to choose the tools that work best for them.
AWS
aws.amazon.com › blogs › machine-learning
Artificial Intelligence
1 day ago - In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape. In this post, we explore how Qbtech streamlined their machine learning (ML) workflow using Amazon SageMaker AI, a fully managed service to build, train and deploy ML models, and AWS Glue, a serverless service that makes data integration simpler, faster, and more cost effective.
Amazon Web Services
aws.amazon.com › products › machine learning › amazon sagemaker ai
Machine Learning Service – Amazon Sagemaker AI – AWS
1 week ago - With SageMaker AI, you can build, train, customize, and deploy AI models at scale using complete development environments, purpose-built training infrastructure, AI agent-guided workflows, and optimized inference capabilities—all with enterprise-grade governance and security controls.
Amazon Web Services
aws.amazon.com › machine learning › amazon sagemaker ai › amazon sagemaker ai resources
Machine Learning Service - Amazon SageMaker AI Resources - AWS
1 week ago - Amazon SageMaker AI helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
Reddit
reddit.com › r/aws › guide for sagemaker ai.
r/aws on Reddit: Guide for Sagemaker AI.
May 24, 2025 -
I am thinking of training LLMs on Sagemaker but want to do it for free with minimal to no expense. I did some digging around and found that i need to create something called as Domain and then create a profile choosing the instance types. I need your help to understand this. Please layout ur thoughts and experiences.
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You can't do any serious AI training for free or minimal to no expense on AWS or any other Cloud provider. It is very heavy on GPU compute and people don't give that away for free. I would recommend deep diving AI training to understand what you're asking about. Sage Maker has some free labs you can play with https://studiolab.sagemaker.aws/
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I genuinely mean this as advice and not to be dismissive, but at this level start with ChatGPT and explore a little bit, then come back with specifics, you'll get good responses. In this state it's too open-ended / unclear. https://chatgpt.com/share/68326261-c5bc-8000-acb0-3974ef6b7ca2
Amazon Web Services
aws.amazon.com › amazon sagemaker ai
Machine Learning Service - Free Amazon SageMaker AI - AWS
1 week ago - Build, train, and deploy machine learning models easily with Amazon SageMaker AI on the AWS Free Tier.
Amazon Web Services
aws.amazon.com › machine learning › amazon sagemaker ai › getting started with amazon sagemaker ai
Getting Started with Machine Learning on Amazon SageMaker AI - AWS
1 week ago - Amazon SageMaker AI helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models. Learn how to get started quickly.
AWS
aws.amazon.com › blogs › machine-learning › building-generative-ai-and-ml-solutions-faster-with-ai-apps-from-aws-partners-using-amazon-sagemaker
Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker | Artificial Intelligence
December 4, 2024 - Within SageMaker Studio, Notebooks and Pipelines, data scientists, ML engineers, and AI researchers can use Comet’s robust tracking and monitoring capabilities to oversee model lifecycles from training through production, bringing transparency and reproducibility to ML workflows. You can access the Comet UI directly from SageMaker Studio and SageMaker Unified Studio without the need to provide additional credentials. The app infrastructure is deployed, managed, and supported by AWS, providing a holistic experience and seamless integration.
Wikipedia
en.wikipedia.org › wiki › Amazon_SageMaker
Amazon SageMaker - Wikipedia
October 12, 2025 - Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. It can be used to deploy ML models on embedded systems and edge-devices. The platform was launched in November 2017.
GeeksforGeeks
geeksforgeeks.org › machine learning › what-is-sagemaker-in-aws
What is SageMaker in AWS? - GeeksforGeeks
1 week ago - Many organizations face challenges in AI development due to the costs of hiring experts and maintaining the necessary infrastructure. AWS SageMaker addresses these challenges by offering integrated tools that automate manual tasks, reduce human error, and minimize hardware expenses.
Reddit
reddit.com › r/aws › sagemaker is terrible - is using ec2 a better alternative?
r/aws on Reddit: SageMaker is Terrible - Is Using EC2 a Better Alternative?
March 7, 2025 -
I’ve been trying to use SageMaker, and honestly, it feels awful. The training and inference workflows force you to use the unnecessary SageMaker Python SDK, and the code editor is terrible, no support for Pylance or other Microsoft tools, making development incredibly difficult. I don’t see any real advantages.
The only thing that seems relatively easy is managing but overall, it feels extremely frustrating.
Would it make more sense to just spin up an EC2 instance, develop models there using VSCode + SSH, and handle deployment directly? Also, would setting up MLflow on EC2 work just as well?
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What "code editor"? The current gen code editor SageMaker offers is basically VS Code which supports any extensions you want. SageMaker instances are "just EC2" plus few goodies. SageMaker has a ton of other features that you can use but don't have to. If you don't like SageMaker SDK you can just click through the web console or use CDK (if you know what it is). Doing all that properly using EC2 will end up being more labourous/tedious in the end of the day.
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The biggest and best companies are all using EKS for ML training and inferencing either with MLflow or their own custom pipelines. Sagemaker is bloated and expensive.
LinkedIn
linkedin.com › all topics
Amazon SageMaker Online Training Courses | LinkedIn Learning, formerly Lynda.com
Our Amazon SageMaker online training courses from LinkedIn Learning (formerly Lynda.com) provide you with the skills you need, from the fundamentals to advanced tips. Browse our wide selection of Amazon SageMaker classes to find exactly what you’re looking for.
Collibra
productresources.collibra.com › docs › collibra › latest › Content › Catalog › IntegrateAIModels › AWSSageMaker › co_about-aws-sagemaker-ai.htm
About the AWS SageMaker AI integration via Edge
When you integrate AWS SageMaker AI, you integrate the metadata of ML models from Amazon SageMaker to Collibra Platform.
YouTube
youtube.com › watch
Amazon SageMaker AI Tutorial - YouTube
In 3 minutes, learn what Amazon SageMaker AI can do for you. ➡️ FULL COURSE: https://www.cloudwolf.com
Published April 11, 2025
DataCamp
datacamp.com › tutorial › aws-sagemaker-tutorial
The Complete Guide to Machine Learning on AWS with Amazon SageMaker | DataCamp
June 19, 2024 - Discover how Amazon SageMaker simplifies machine learning workflows. Learn about building, training, and deploying models on AWS with this fully managed service.
AWS re:Post
repost.aws › questions › QUTgC3561MQKaWwyazLh_YXQ › sagemaker-costs-for-ai-model
SageMaker costs for AI model | AWS re:Post
March 18, 2025 - I recently deployed anymodality/llava-v1.5-7b from Hugging face onto Amazon SageMaker for inference. I created a model.tar.gz file which was about 9 GB and uploaded to an S3 bucket to hold my model. The model was deployed for region west 2 with: predictor = huggingface_model.deploy( initial_instance_count=1, instance_type="ml.g5.xlarge", ) And now I have an endpoint that I can request to with an image url and text prompt to generate an AI image description. I'm totally new to AWS and SageMaker, I tried reading through the pricing but I dont quite understand it.
Medium
medium.com › @tdhtp2016 › aws-machine-learning-and-generative-ai-sagemaker-and-bedr-256512b5514f
AWS Machine Learning and Generative AI — SageMaker and Bedrock | by Duy Hưng | Medium
May 30, 2024 - Serverless ML: AWS is making it easier to build, train, and deploy ML models without the need to manage servers. With services like Amazon SageMaker, customers can build and train models using managed Jupyter Notebook and then deploy them to a serverless endpoint with just a few clicks.
Amazon Web Services
aws.amazon.com › analytics › amazon sagemaker › amazon sagemaker catalog
Amazon SageMaker Data and AI Governance - AWS
November 14, 2025 - The next generation of Amazon SageMaker simplifies the discovery, governance, and collaboration for data and AI across your structured and unstructured data, AI models, business intelligence dashboards, and applications.