Difference between revisions of "SageMaker"
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| − | [ | + | {{#seo: |
| − | [ | + | |title=PRIMO.ai |
| + | |titlemode=append | ||
| + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS | ||
| + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
| + | }} | ||
| + | [https://www.youtube.com/results?search_query=sagemaker+aws+amazon YouTube] | ||
| + | [https://www.quora.com/search?q=sagemaker%20aws%20amazon ... Quora] | ||
| + | [https://www.google.com/search?q=sagemaker+aws+amazon ...Google search] | ||
| + | [https://news.google.com/search?q=sagemaker+aws+amazon ...Google News] | ||
| + | [https://www.bing.com/news/search?q=sagemaker+aws+amazon&qft=interval%3d%228%22 ...Bing News] | ||
| − | * [ | + | * [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]] |
| + | ** [[SageMaker]] | ||
| + | * [[Bedrock]] | [[Amazon]] | ||
| + | * [[Hugging Face#LightGPT|LightGPT]] | [[Hugging Face]] | ||
| + | * [[Foundation Models (FM)]] | ||
| + | * [https://docs.aws.amazon.com/sagemaker/latest/dg/tf-examples.html Product page...] | ||
* [[DeepLens - deep learning enabled video camera]] | * [[DeepLens - deep learning enabled video camera]] | ||
| − | * [[Notebooks; Jupyter and R Markdown]] | + | * [[Amazon | Amazon AWS]] |
| + | * [[Notebooks]]; [[Jupyter]] and R Markdown | ||
| + | * [https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html Create an Amazon SageMaker Autopilot Experiment in SageMaker Studio] | ||
| + | ** [https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-sharing.html Sharing SageMaker notebooks] | ||
| + | ** [https://github.com/FINRAOS/CodeSamples/tree/master/machine-learning-samples/sagemaker SageMaker notebooks | Fintech] | ||
| + | * [https://medium.com/@manasnarkar/sagemaker-how-to-build-machine-learning-pipeline-f485071498dd SageMaker : How to build your Machine Learning Pipeline | Manas Narkar - Medium] | ||
https://d2908q01vomqb2.cloudfront.net/da4b9237bacccdf19c0760cab7aec4a8359010b0/2017/11/29/please_work.png | https://d2908q01vomqb2.cloudfront.net/da4b9237bacccdf19c0760cab7aec4a8359010b0/2017/11/29/please_work.png | ||
| − | + | 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. [https://geekflare.com/machine-learning-paas/ 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 == | == Getting Started == | ||
| − | * [ | + | * [https://aws.amazon.com/sagemaker/studio-lab/ 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. |
| + | * [https://www.deeplearning.team Deep Learning Team | Jovon Weathers] | ||
<youtube>tBRHh_V8vjc</youtube> | <youtube>tBRHh_V8vjc</youtube> | ||
| + | <youtube>qMEtqJPhqpA</youtube> | ||
== More == | == More == | ||
Latest revision as of 05:14, 15 June 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- 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
