Difference between revisions of "SageMaker"
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** [[SageMaker]] | ** [[SageMaker]] | ||
* [[Bedrock]] | [[Amazon]] | * [[Bedrock]] | [[Amazon]] | ||
| + | * [[Hugging Face#LightGPT|LightGPT]] | [[Hugging Face]] | ||
* [[Foundation Models (FM)]] | * [[Foundation Models (FM)]] | ||
* [https://docs.aws.amazon.com/sagemaker/latest/dg/tf-examples.html Product page...] | * [https://docs.aws.amazon.com/sagemaker/latest/dg/tf-examples.html Product page...] | ||
Latest revision as of 05:14, 15 June 2023
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- Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)
- Bedrock | Amazon
- LightGPT | Hugging Face
- Foundation Models (FM)
- Product page...
- DeepLens - deep learning enabled video camera
- Amazon AWS
- Notebooks; Jupyter and R Markdown
- Create an Amazon SageMaker Autopilot Experiment in SageMaker Studio
- SageMaker : How to build your Machine Learning Pipeline | Manas Narkar - Medium
The platform provides a jump start to data scientists and AI developers to build their models, utilize the models from the community, and code right on the platform. Amazon Sagemaker provides you with a scalable cloud computing platform to build, train, and deploy machine learning models quickly. 7 Platform-as-a-Service (PaaS) for Machine Learning and AI Developers | Abhishek Kothari - GeekFlare
Major benefits of using Amazon Sagemaker are:
- Readily available pre-built algorithms for use
- Gives you a jump start with primary installations and setup did for you
- Allows you to scale up quickly and train models faster
- Provides popular Jupyter Notebook like interface to perform all relevant operations on a single platform
- Provides an auto-pilot functionality to auto train your models
- A massive repository of high quality pre-trained data for training your models faster
- Straightforward collaboration with fellow data scientists by sharing the web platform
Getting Started
- SageMaker Studio Lab | Amazon; a free offering to assist developers master machine learning techniques and experimenting with the technology. Users get everything they need to start with Studio Lab, including a JupyterLab IDE, model training on CPUs and GPUs, and 15 GB of persistent storage.
- Deep Learning Team | Jovon Weathers
More
