├── img
    ├── blog.png
    ├── blog.webp
    └── awesome-sagemaker-intro.svg
├── CODE_OF_CONDUCT.md
├── .github
    └── pull_request_template.md
├── LICENSE
├── low_code_no_code_ml.md
├── .gitignore
├── data_preparation.md
├── learning_sagemaker.md
├── deploying_ml_models.md
├── getting_started.md
├── CONTRIBUTING.md
├── ml_domains.md
├── mlops.md
├── README.md
└── generative_ai.md


/img/blog.png:
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https://raw.githubusercontent.com/aws-samples/awesome-sagemaker/HEAD/img/blog.png


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/img/blog.webp:
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/CODE_OF_CONDUCT.md:
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1 | ## Code of Conduct
2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
4 | opensource-codeofconduct@amazon.com with any additional questions or comments.
5 | 


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/.github/pull_request_template.md:
--------------------------------------------------------------------------------
 1 | # Description
 2 | 
 3 | Please include a summary of the changes and the related issue. Please also include relevant motivation and context. List any dependencies that are required for this change.
 4 | 
 5 | Please make sure your entry is respecting the format defined in the [contribution guidelines](./../CONTRIBUTING.md).
 6 | 
 7 | ## Topic Area
 8 | 
 9 | Please select the area where you want to include the content.
10 | 
11 | - [] Getting Started
12 | - [] Building ML Models
13 | - [] Deploying ML Models
14 | - [] MLOps
15 | - [] Low Code/ No Code ML
16 | - [] ML Governance
17 | - [] Responsible AI
18 | - [] ML Domains
19 | - [] Learning SageMaker
20 | 
21 | ## Content Type
22 | 
23 | - [] Blog
24 | - [] Code
25 | - [] Docs
26 | - [] Video
27 | - [] Workshop


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 2 | 
 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of
 4 | this software and associated documentation files (the "Software"), to deal in
 5 | the Software without restriction, including without limitation the rights to
 6 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
 7 | the Software, and to permit persons to whom the Software is furnished to do so.
 8 | 
 9 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
10 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
11 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
12 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
13 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
14 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


--------------------------------------------------------------------------------
/low_code_no_code_ml.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Flow_code_no_code_ml.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## Low Code - No Code
 6 | 
 7 | - [Docs] [SageMaker JumpStart Available Model Table](https://sagemaker.readthedocs.io/en/v2.82.0/doc_utils/jumpstart.html)
 8 | - [Blog] [Implementing MLOps practices with Amazon SageMaker JumpStart pre-trained models](https://aws.amazon.com/blogs/machine-learning/implementing-mlops-practices-with-amazon-sagemaker-jumpstart-pre-trained-models/)
 9 | - [Video] [Introduction to Amazon SageMaker Canvas](https://www.youtube.com/watch?v=Sy3GDQT6Lnk)
10 | - [Workshop] [SageMaker Canvas Immersion Day](https://catalog.us-east-1.prod.workshops.aws/workshops/80ba0ea5-7cf9-4b8c-9d3f-1cd988b6c071/en-US)
11 | - [Workshop] [AWS Machine Learning Low-Code Immersion Day](https://catalog.us-east-1.prod.workshops.aws/workshops/f560a788-af64-4e5a-a02c-a6c88516ab02/en-US/)
12 | 
13 | ## AutoML
14 | - [Code] [Direct Marketing with Amazon SageMaker Autopilot](https://github.com/aws/amazon-sagemaker-examples/blob/main/autopilot/sagemaker_autopilot_direct_marketing.ipynb)
15 | - [Video] [Using AutoML to create high-quality models with just a few clicks](https://www.youtube.com/watch?v=f9aCwmVWvC8)
16 | - [Video] [Using AutoML for Common Financial Services Use Cases](https://www.youtube.com/watch?v=r2-VmuUh7jM)
17 | 
18 | ## Data Wrangler
19 | - [Code] [Amazon SageMaker Data Wrangler for Diabetic Patient Readmission Prediction](https://github.com/aws-samples/amazon-sagemaker-data-wrangler-hospital-readmission-prediction)
20 | - [Video] [External] [Using SageMaker Data Wrangler to process a stroke prediction dataset](https://www.youtube.com/watch?v=AEYSNNiIq-k)
21 | - [Video] [External] [Using advanced import and export options with SageMaker Data Wrangler](https://www.youtube.com/watch?v=O5W-tvbQ664)
22 | 


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/.gitignore:
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  1 | # Created by .ignore support plugin (hsz.mobi)
  2 | ### Python template
  3 | # Byte-compiled / optimized / DLL files
  4 | __pycache__/
  5 | *.py[cod]
  6 | *$py.class
  7 | 
  8 | # C extensions
  9 | #*.so
 10 | 
 11 | # Distribution / packaging
 12 | .Python
 13 | build/
 14 | develop-eggs/
 15 | dist/
 16 | downloads/
 17 | eggs/
 18 | .eggs/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | .hypothesis/
 51 | .pytest_cache/
 52 | 
 53 | # Translations
 54 | *.mo
 55 | *.pot
 56 | 
 57 | # Django stuff:
 58 | *.log
 59 | local_settings.py
 60 | db.sqlite3
 61 | db.sqlite3-journal
 62 | 
 63 | # Flask stuff:
 64 | instance/
 65 | .webassets-cache
 66 | 
 67 | # Scrapy stuff:
 68 | .scrapy
 69 | 
 70 | # Sphinx documentation
 71 | docs/_build/
 72 | 
 73 | # PyBuilder
 74 | target/
 75 | 
 76 | # Jupyter Notebook
 77 | .ipynb_checkpoints
 78 | 
 79 | # IPython
 80 | profile_default/
 81 | ipython_config.py
 82 | 
 83 | # pyenv
 84 | .python-version
 85 | 
 86 | # pipenv
 87 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 88 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 89 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 90 | #   install all needed dependencies.
 91 | #Pipfile.lock
 92 | 
 93 | # celery beat schedule file
 94 | celerybeat-schedule
 95 | 
 96 | # SageMath parsed files
 97 | *.sage.py
 98 | 
 99 | # Environments
100 | .env
101 | .venv
102 | env/
103 | venv/
104 | ENV/
105 | env.bak/
106 | venv.bak/
107 | 
108 | # Spyder project settings
109 | .spyderproject
110 | .spyproject
111 | 
112 | # Rope project settings
113 | .ropeproject
114 | 
115 | # mkdocs documentation
116 | /site
117 | 
118 | # mypy
119 | .mypy_cache/
120 | .dmypy.json
121 | dmypy.json
122 | 
123 | # Pyre type checker
124 | .pyre/
125 | 
126 | .idea/
127 | .vscode/
128 | deploy/
129 | test/
130 | **/.DS_Store
131 | cdk.out/


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/data_preparation.md:
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 1 | 
 2 | <div align="center">
 3 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhttps%2F%2Fgithub.com%2FMithilShah%2Fawesome-sagemaker%2Fblob%2Fmain%2Fdata_preparation.mdgithub.com%2Fgjbae1212%2Fhit-counter&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 4 | </div>
 5 | 
 6 | 
 7 | ## Data Preparation
 8 | 
 9 | ### Data Processing
10 | - [Docs] [SageMaker Processing](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html#amazon-sagemaker-processing)
11 | - [Code] [Spark Containers](https://github.com/aws/sagemaker-spark-container/blob/master/available_images.md)
12 | 
13 | ### Large Scale Data Processing
14 | - [Docs] [Spark in Processing Jobs](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html#data-processing-with-spark)
15 | - [Docs] [Using Dask with SageMaker Processing](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker_processing/feature_transformation_with_sagemaker_processing_dask/feature_transformation_with_sagemaker_processing_dask.html)
16 | - [Blog] [Prepare data at scale in Amazon SageMaker Studio using serverless AWS Glue interactive sessions](https://aws.amazon.com/blogs/machine-learning/prepare-data-at-scale-in-amazon-sagemaker-studio-using-serverless-aws-glue-interactive-sessions/)
17 | - [Blog] [External] [Automating Visualization Reports with SageMaker Studio Interactive Session and Notebook Jobs](https://medium.com/@brn.pistone/streamlining-data-insights-automating-visualization-reports-with-sagemaker-studio-interactive-37d5d49480a3)
18 | - [Blog] [Build accurate ML training datasets using point-in-time queries with Amazon SageMaker Feature Store and Apache Spark](https://aws.amazon.com/blogs/machine-learning/build-accurate-ml-training-datasets-using-point-in-time-queries-with-amazon-sagemaker-feature-store-and-apache-spark/)
19 | - [Workshop] [SageMaker Studio integration with EMR Workshop](https://catalog.workshops.aws/sagemaker-studio-emr/en-US)
20 | - [Video] [Using Apache Spark on Amazon EMR with SageMaker](https://www.youtube.com/watch?v=RxRENYQBxZU)
21 | - [Video] [Scalable data preparation using Amazon SageMaker Studio notebooks - AWS Virtual Workshop](https://www.youtube.com/watch?v=UcRNNHuYsxE)
22 | 
23 | ### Data Labeling
24 | - [Video] [Introducing Amazon SageMaker Ground Truth Plus](https://www.youtube.com/watch?v=Y3Lo63yiqsU)
25 | - [Blog] [External] [Labeling data with Label Studio on SageMaker](https://medium.com/geekculture/labeling-data-with-label-studio-on-sagemaker-e4b2d1b562f7)
26 | 


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/learning_sagemaker.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Flearning_sagemaker.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## Learning SageMaker
 6 | 
 7 | ### Certification
 8 | - [Docs] [AWS Certified Machine Learning - Specialty](https://aws.amazon.com/certification/certified-machine-learning-specialty/)
 9 | 
10 | ### MOOCs
11 | - [Video] [External] [Getting Started with AWS Machine Learning](https://www.coursera.org/learn/aws-machine-learning)
12 | - [Video] [External] [Practical Data Science on the AWS Cloud Specialization](https://www.coursera.org/specializations/practical-data-science)
13 | - [Video] [Machine Learning University](https://aws.amazon.com/machine-learning/mlu/)
14 | 
15 | ### Digital & Classroom
16 | - [Video] [External] [Machine Learning Learning Plan](https://explore.skillbuilder.aws/learn/public/learning_plan/view/28/machine-learning-learning-plan)
17 | - [Video] [AWS Classroom Training](https://aws.amazon.com/training/classroom/?nc2=sb_tr_ct)
18 | 
19 | ### Tutorials
20 | - [Docs] [AWS hands-on tutorials](https://aws.amazon.com/getting-started/hands-on/?getting-started-all.sort-by=item.additionalFields.sortOrder&getting-started-all.sort-order=asc&awsf.getting-started-category=category%23machine-learning&awsf.getting-started-level=*all&awsf.getting-started-content-type=*all)
21 | 
22 | ### Community
23 | - [Docs] [AWS re:Post](https://repost.aws/)
24 | - [Docs] [Stack Overflow](https://stackoverflow.com/questions/tagged/amazon-sagemaker)
25 | - [Blog] [AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligence/sagemaker/)
26 | - [Code] [Data Science on AWS GitHub](https://github.com/data-science-on-aws/data-science-on-aws)
27 | - [Video] [DataScienceOnAWS](https://www.youtube.com/channel/UCvlZKtekcKkBUuz8f9dhobw/featured)
28 | - [Video] [Amazon SageMaker Fridays](https://pages.awscloud.com/SageMakerFridays)
29 | - [Video] [AWS ML School](https://mlschool.splashthat.com/?sc_channel=sm&sc_campaign=Machine_Learning&sc_publisher=LINKEDIN&sc_geo=GLOBAL&sc_outcome=awareness&trk=machine_learning)
30 | - [Video] [AWS events](https://aws.amazon.com/events/)
31 | 
32 | 
33 | ### Books
34 | - [Docs] [Learn Amazon SageMaker - Second Edition](https://www.packtpub.com/product/learn-amazon-sagemaker-second-edition/9781801817950)
35 | - [Docs] [Data Science on AWS](https://www.oreilly.com/library/view/data-science-on/9781492079385/)
36 | 
37 | ### News
38 | - [Docs] [What's New with Machine Learning on AWS?](https://aws.amazon.com/about-aws/whats-new/machine-learning/?whats-new-content.sort-by=item.additionalFields.postDateTime&whats-new-content.sort-order=desc&awsf.whats-new-products=general-products%23amazon-sagemaker)


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/deploying_ml_models.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Fdeploying_ml_models.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## Deploying ML Models
 6 | 
 7 | ### Inference
 8 | - [Blog] [Secure multi-account model deployment with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/part-1-secure-multi-account-model-deployment-with-amazon-sagemaker/)
 9 | - [Blog] [External] [An Amazon SageMaker Inference comparison with Hugging Face Transformers](https://www.philschmid.de/sagemaker-inference-comparison)
10 | - [Code] [Optimize SageMaker Endpoint Auto scaling using Inference recommender](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-inference-recommender/auto-scaling/optimize_endpoint_scaling.ipynb)
11 | - [Video] [Amazon SageMaker Inference explained: Which style is right for you?](https://www.youtube.com/watch?v=bRUNpuRGeZc)
12 | - [Video] [Introduction to Amazon SageMaker Serverless Inference](https://www.youtube.com/watch?v=xIp2305saII)
13 | - [Video] [External] [Running Triton Inference Server with Amazon SageMaker](https://www.coursera.org/lecture/machine-learning-aws-nvidia/running-triton-inference-server-with-amazon-sagemaker-HwIki)
14 | 
15 | ### Hardware Acceleration
16 | - [Blog] [External] [Choosing the right GPU for deep learning on AWS](https://towardsdatascience.com/choosing-the-right-gpu-for-deep-learning-on-aws-d69c157d8c86)
17 | - [Blog] [Speed up BERT inference with Hugging Face Transformers and AWS Inferentia](https://www.philschmid.de/huggingface-bert-aws-inferentia)
18 | 
19 | ### Edge Deployments
20 | - [Blog] [Using Amazon SageMaker Edge Manager and AWS IoT Greengrass V2](https://aws.amazon.com/blogs/machine-learning/build-machine-learning-at-the-edge-applications-using-amazon-sagemaker-edge-manager-and-aws-iot-greengrass-v2/)
21 | - [Blog] [MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass](https://aws.amazon.com/blogs/machine-learning/mlops-at-the-edge-with-amazon-sagemaker-edge-manager-and-aws-iot-greengrass/)
22 | - [Code] [ML@Edge with SageMaker Neo & Edge Manager - Getting Started](https://github.com/aws-samples/ml-edge-getting-started)
23 | - [Code] [ML@Edge with SageMaker Edge Manager - Workshop](https://github.com/aws-samples/amazon-sagemaker-edge-manager-workshop)
24 | - [Video] [Industrial defect detection with computer vision using Amazon SageMaker ](https://www.youtube.com/watch?v=v6OHL3LTjkA)
25 | 
26 | ### Debugging
27 | - [Docs] [Use TensorBoard in Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-tensorboard.html)
28 | - [Docs] [Visualize Amazon SageMaker Training Jobs with TensorBoard](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-python-sdk/tensorboard_keras/tensorboard_keras.html)
29 | - [Video] [Deep Dive on Amazon SageMaker Debugger & Amazon SageMaker Model Monitor](https://www.youtube.com/watch?v=0zqoeZxakOI)


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/getting_started.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Fgetting_started.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## Getting Started
 6 | 
 7 | ### Introduction
 8 | - [Docs] [Getting started with Amazon SageMaker Tutorials](https://aws.amazon.com/sagemaker/getting-started/)
 9 | - [Docs] [SageMaker Example Notebooks](https://sagemaker-examples.readthedocs.io/en/latest/)
10 | - [Workshop] [SageMaker Immersion Day (hands-on labs)](https://catalog.us-east-1.prod.workshops.aws/workshops/63069e26-921c-4ce1-9cc7-dd882ff62575/en-US)
11 | 
12 | ### Developer Experience
13 | - [Blog] [Host VS code-server on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/host-code-server-on-amazon-sagemaker/)
14 | - [Blog] [Boost Your ML Team’s Productivity with Container-Based Development in the Cloud](https://medium.com/towards-data-science/boost-your-ml-teams-productivity-with-container-based-development-in-the-cloud-56aa35552776)
15 | - [Code] [SageMaker SSH Helper - Connect into SageMaker with AWS System Manager / SSH](https://github.com/aws-samples/sagemaker-ssh-helper)
16 | 
17 | ### Architecture Best Practices
18 | - [Docs] [SageMaker Studio Administration Best Practices](https://docs.aws.amazon.com/whitepapers/latest/sagemaker-studio-admin-best-practices/sagemaker-studio-admin-best-practices.html)
19 | - [Blog] [AWS Well-Architected Machine Learning Lens](https://aws.amazon.com/blogs/architecture/introducing-the-new-aws-well-architected-machine-learning-lens/)
20 | - [Docs] [AWS Account Management Reference Guide](https://docs.aws.amazon.com/accounts/latest/reference/welcome-multiple-accounts.html)
21 | 
22 | ### ML Platform Setup
23 | - [Blog] [Dive deep into Amazon SageMaker Studio Notebooks architecture](https://aws.amazon.com/blogs/machine-learning/dive-deep-into-amazon-sagemaker-studio-notebook-architecture/)
24 | - [Blog] [Separate lines of business or teams with multiple Amazon SageMaker Studio domains](https://aws.amazon.com/blogs/machine-learning/separate-lines-of-business-or-teams-with-multiple-amazon-sagemaker-domains/)
25 | - [Blog] [Organize machine learning development using shared spaces in SageMaker Studio for real-time collaboration](https://aws.amazon.com/blogs/machine-learning/organize-machine-learning-development-using-shared-spaces-in-sagemaker-studio-for-real-time-collaboration/)
26 | - [Blog] [Team and user management with Amazon SageMaker and AWS SSO](https://aws.amazon.com/blogs/machine-learning/team-and-user-management-with-amazon-sagemaker-and-aws-sso/)
27 | - [Blog] [Secure AWS CodeArtifact access for isolated Amazon SageMaker notebook instances](https://aws.amazon.com/blogs/machine-learning/secure-aws-codeartifact-access-for-isolated-amazon-sagemaker-notebook-instances/)
28 | - [Blog] [Private package installation in Amazon SageMaker running in internet-free mode](https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon-sagemaker-running-in-internet-free-mode/)
29 | - [Video] [Access SageMaker Studio via an external identity provider](https://www.youtube.com/watch?v=9CnFrSqvXYM)
30 | - [Video] [Onboard Quickly to Amazon SageMaker Studio](https://www.youtube.com/watch?v=wiDHCWVrjCU)
31 | 


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/CONTRIBUTING.md:
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 1 | # Contributing Guidelines
 2 | 
 3 | Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
 4 | documentation, we greatly value feedback and contributions from our community.
 5 | 
 6 | Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
 7 | information to effectively respond to your bug report or contribution.
 8 | 
 9 | 
10 | ## Reporting Bugs/Feature Requests
11 | 
12 | We welcome you to use the GitHub issue tracker to report bugs or suggest features.
13 | 
14 | When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already
15 | reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
16 | 
17 | * A reproducible test case or series of steps
18 | * The version of our code being used
19 | * Any modifications you've made relevant to the bug
20 | * Anything unusual about your environment or deployment
21 | 
22 | 
23 | ## Contributing via Pull Requests
24 | Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
25 | 
26 | 1. You are working against the latest source on the *main* branch.
27 | 2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
28 | 3. You open an issue to discuss any significant work - we would hate for your time to be wasted.
29 | 
30 | To send us a pull request, please:
31 | 
32 | 1. Fork the repository.
33 | 2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
34 | 3. Ensure local tests pass.
35 | 4. Commit to your fork using clear commit messages.
36 | 5. Send us a pull request, answering any default questions in the pull request interface.
37 | 6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
38 | 
39 | GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
40 | [creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
41 | 
42 | 
43 | ### Add new content in the Awesome SageMaker list
44 | 
45 | 1. Make sure that the content is not already included in other sections of the list
46 | 2. New content must be marked with the appropriate tag
47 |     * Blog
48 |     * Code
49 |     * Docs
50 |     * Video
51 |     * Workshop
52 | 3. New entry has to respect the following format:
53 | 
54 | ```
55 | [<CONTENT_TYPE>] [<CONTENT_TITLE>](<CONTENT_URL>)
56 | ```
57 | 
58 | #### Example:
59 | 
60 | ```
61 | [Blog] [AWS Blog](https://aws.amazon.com/blogs/aws/)
62 | ```
63 | 
64 | 4. Content that doesn't belong to official AWS resources must be marked as *External*
65 | 
66 | ```
67 | [<CONTENT_TYPE>] [External] [<CONTENT_TITLE>](<CONTENT_URL>)
68 | ```
69 | 
70 | #### Example:
71 | 
72 | ```
73 | [Blog] [External] [My Blog](https://mockurlforblog)
74 | ```
75 | 
76 | 
77 | 
78 | 
79 | 
80 | ## Finding contributions to work on
81 | Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
82 | 
83 | 
84 | ## Code of Conduct
85 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
86 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
87 | opensource-codeofconduct@amazon.com with any additional questions or comments.
88 | 
89 | 
90 | ## Security issue notifications
91 | If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
92 | 
93 | 
94 | ## Licensing
95 | 
96 | See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
97 | 


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/ml_domains.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Fml_domains.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## ML Domains
 6 | 
 7 | ### Responsible AI
 8 | - [Video] [Build trustworthy ML detection and monitor bias in ML models](https://www.youtube.com/watch?v=6BZropfX6hA)
 9 | 
10 | ### ML Governance
11 | #### Model Management
12 | - [Blog] [Improve governance of your machine learning models with Amazon SageMaker - Model Cards & Dashboard](https://aws.amazon.com/blogs/machine-learning/improve-governance-of-your-machine-learning-models-with-amazon-sagemaker/)
13 | 
14 | #### Security
15 | - [Docs] [Security in Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/security.html)
16 | - [Docs] [Amazon SageMaker Role Manager -  least-privilege access needs required for personas for ML personas](https://docs.aws.amazon.com/sagemaker/latest/dg/role-manager.html)
17 | 
18 | #### Cost Tracking & Control
19 | - [Docs] [SageMaker Pricing](https://aws.amazon.com/sagemaker/pricing/)
20 | - [Blog] [Decrease Your Machine Learning Costs with Instance Price Reductions and Savings Plans for Amazon SageMaker](https://aws.amazon.com/blogs/aws/slash-your-machine-learning-costs-with-instance-price-reductions-and-savings-plans-for-amazon-sagemaker/)
21 | - [Docs] [Machine Learning Savings Plans](https://aws.amazon.com/savingsplans/ml-pricing/)
22 | - [Blog] [Automatically shutdown idle resources on SageMaker Studio](https://aws.amazon.com/blogs/machine-learning/save-costs-by-automatically-shutting-down-idle-resources-within-amazon-sagemaker-studio/)
23 | - [Blog] [Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/set-up-enterprise-level-cost-allocation-for-ml-environments-and-workloads-using-resource-tagging-in-amazon-sagemaker/)
24 |   
25 | ### Computer Vision
26 | - [Blog] [Using the TensorFlow 2 Object Detection API with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/training-and-deploying-models-using-tensorflow-2-with-the-object-detection-api-on-amazon-sagemaker/)
27 | - [Blog] [Object detection with Detectron2 on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/object-detection-with-detectron2-on-amazon-sagemaker/)
28 | - [Blog] [External] [Semantic Segmantion with Hugging Face's Transformers & Amazon SageMaker](https://www.philschmid.de/image-segmentation-sagemaker)
29 | - [Blog] [Build a medical image analysis pipeline on Amazon SageMaker using the MONAI framework](https://aws.amazon.com/blogs/industries/build-a-medical-image-analysis-pipeline-on-amazon-sagemaker-using-the-monai-framework/)
30 | - [Blog] [Scalable Medical Computer Vision Model Training with Amazon SageMaker](https://aws.amazon.com/blogs/industries/scalable-medical-computer-vision-model-training-with-amazon-sagemaker-part-1/)
31 | 
32 | ### Natural Language Processing
33 | - [Docs] [External] [Hugging Face on Amazon SageMaker](https://huggingface.co/docs/sagemaker/main)
34 | - [Blog] [External] [Unlock the Latest Transformer Models with Amazon SageMaker](https://towardsdatascience.com/unlock-the-latest-transformer-models-with-amazon-sagemaker-7fe65130d993)
35 | - [Video] [Hugging Face on Amazon SageMaker Tutorial - Part 1](https://www.youtube.com/watch?v=80ix-IyNnQI)
36 | - [Video] [Hugging Face on Amazon SageMaker Tutorial - Part 2](https://www.youtube.com/watch?v=BqQ14SZ5tos)
37 | - [Video] [Hugging Face on Amazon SageMaker Tutorial - Part 3](https://www.youtube.com/watch?v=oVIvXfeunv8)
38 | - [Code] [External] [Hugging Face Transformers Amazon SageMaker Examples](https://github.com/huggingface/notebooks/tree/main/sagemaker)
39 | 
40 | ### Audio
41 | - [Blog] [External] [Automatic Speech Recogntion with HuggingFace's Transformers & Amazon SageMaker](https://www.philschmid.de/automatic-speech-recognition-sagemaker)
42 | 
43 | ### R
44 | - [Blog] [RStudio on SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/rstudio.html)
45 | - [Blog] [External] [Overview of possible ways of running R workloads on SageMaker](https://towardsdatascience.com/3-1-ways-of-running-r-on-amazon-sagemaker-13034a8f3686)
46 | - [Blog] [Create Amazon SageMaker model building pipelines and deploy R models using RStudio on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/create-amazon-sagemaker-model-building-pipelines-and-deploy-r-models-using-rstudio-on-amazon-sagemaker/)
47 | - [Blog] [External] [Create reusable containers for R](https://towardsdatascience.com/how-to-create-reusable-r-containers-for-sagemaker-jobs-a3d481daf5cd)
48 | 


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/mlops.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Fmlops.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## MLOps
 6 | 
 7 | ### MLOps Foundations
 8 | - [Blog] [Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions](https://aws.amazon.com/blogs/machine-learning/build-an-end-to-end-mlops-pipeline-using-amazon-sagemaker-pipelines-github-and-github-actions/)
 9 | - [Code] [Deep Learning MLOps workshop with Amazon SageMaker](https://catalog.us-east-1.prod.workshops.aws/workshops/47906c57-854e-4c73-abdb-6b49fe364370/en-US)
10 | - [Code] [SageMaker MLOps Multi Account Setup with GitHub and Terraform](https://github.com/aws-samples/mlops-multi-account-terraform)
11 | - [Blog] [MLOps foundation roadmap for enterprises with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/mlops-foundation-roadmap-for-enterprises-with-amazon-sagemaker/)
12 | - [Code] [SageMaker Projects Repo for MLOps](https://github.com/aws-samples/sagemaker-custom-project-templates)
13 | - [Video] [Automate MLOps with SageMaker Projects](https://www.youtube.com/watch?v=3_cHnk9VSfQ)
14 | 
15 | ### SageMaker Pipelines
16 | - [Blog] [Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines](https://aws.amazon.com/blogs/machine-learning/building-automating-managing-and-scaling-ml-workflows-using-amazon-sagemaker-pipelines/)
17 | - [Blog] [Multi-branch training MLOps pipeline using AWS](https://aws.amazon.com/blogs/machine-learning/improve-your-data-science-workflow-with-a-multi-branch-training-mlops-pipeline-using-aws/)
18 | - [Code] [Amazon SageMaker Pipelines Workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/1bb7ba03-e533-464f-8726-91a74513b1a1/en-US/introduction)
19 | 
20 | ### Using Third-Party
21 | - [Blog] [Create Amazon SageMaker projects using third-party source control and Jenkins](https://aws.amazon.com/blogs/machine-learning/create-amazon-sagemaker-projects-using-third-party-source-control-and-jenkins/)
22 | - [Blog] [Build MLOps workflows with Amazon SageMaker projects, GitLab, and GitLab pipelines](https://aws.amazon.com/blogs/machine-learning/build-mlops-workflows-with-amazon-sagemaker-projects-gitlab-and-gitlab-pipelines/)
23 | - [Blog] [External] [5 Simple Steps to MLOps with GitHub Actions, MLflow, and SageMaker Pipelines](https://medium.com/p/19abf951a70)
24 | - [Blog] [External] [MLOps with MLFlow and Amazon SageMaker Pipelines](https://towardsdatascience.com/mlops-with-mlflow-and-amazon-sagemaker-pipelines-33e13d43f238)
25 | - [Blog] [External] [Scaling MLOps with resilient pipelines](https://towardsdatascience.com/i-tried-scaling-sagemaker-pipeline-executions-and-this-happened-31279b92821e)
26 | 
27 | ### Experiment Tracking & Model Registry
28 | - [Blog] [Managing your machine learning lifecycle with MLflow and Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/managing-your-machine-learning-lifecycle-with-mlflow-and-amazon-sagemaker/)
29 | - [Blog] [Improve ML developer productivity with Weights & Biases and Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/improve-ml-developer-productivity-with-weights-biases-a-computer-vision-example-on-amazon-sagemaker/)
30 | 
31 | ### Data Versioning & Feature Store
32 | - [Blog] [Understanding the key capabilities of Amazon SageMaker Feature Store](https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/)
33 | - [Blog] [Track your ML experiments with DVC and Amazon SageMaker Experiments](https://aws.amazon.com/blogs/machine-learning/track-your-ml-experiments-end-to-end-with-data-version-control-and-amazon-sagemaker-experiments/)
34 | - [Blog] [Scale ML feature ingestion using Amazon SageMaker Feature Store](https://aws.amazon.com/blogs/machine-learning/scale-ml-feature-ingestion-using-amazon-sagemaker-feature-store/)
35 | - [Blog] [Extend model lineage to include ML features using Amazon SageMaker Feature Store](https://aws.amazon.com/blogs/machine-learning/extend-model-lineage-to-include-ml-features-using-amazon-sagemaker-feature-store/)
36 | - [Blog] [Control access to Amazon SageMaker Feature Store offline using AWS Lake Formation](https://aws.amazon.com/blogs/machine-learning/control-access-to-amazon-sagemaker-feature-store-offline-using-aws-lake-formation/)
37 | - [Blog] [Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction](https://aws.amazon.com/blogs/machine-learning/speed-ml-development-using-sagemaker-feature-store-and-apache-iceberg-offline-store-compaction/)
38 | - [Code] [Amazon SageMaker Feature Store Workshop](https://github.com/aws-samples/amazon-sagemaker-feature-store-end-to-end-workshop)
39 | 
40 | ### Model Monitoring
41 | - [Docs] [Monitor models for data and model quality, bias, and explainability - Real Time & Batch](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html)
42 | - [Blog] [Retrain your model when data drift is detected](https://aws.amazon.com/blogs/machine-learning/automate-model-retraining-with-amazon-sagemaker-pipelines-when-drift-is-detected/)
43 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | <div align="center">
 2 |   <a href="https://aws.amazon.com/sagemaker/">
 3 |   <img width="250" height="250"  src="img/awesome-sagemaker-intro.svg" alt="SageMaker"></a>
 4 | </div>
 5 | <h1 align="center">
 6 | 	AWSome SageMaker
 7 | </h1>
 8 | <div align="center">
 9 |   <a href="https://github.com/sindresorhus/awesome">
10 |   <img src="https://awesome.re/badge.svg" alt="Awesome">
11 |   </a>
12 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fsofianhamiti%2Fawesome-sagemaker&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
13 | </div>
14 | 
15 | > A curated list of awesome references for Amazon SageMaker.
16 | 
17 | :ledger: The curated list consists of the following sections.  
18 | 
19 | * [**Getting Started**](./getting_started.md)  - Start here if you are setting up Sagemaker (including studio)
20 |   - [Introduction](./getting_started.md#introduction)
21 |   - [Developer Experience](./getting_started.md#developer-experience)
22 |   - [Architecture Best Practices](./getting_started.md#architecture-best-practices) 
23 |   - [ML Platform Setup](./getting_started.md#ml-platform-setup)
24 | 
25 | * [**Data Preparation**](./data_preparation.md) - Understand the options to prepare data for machine learning  
26 |   - [Data Processing](./data_preparation.md#data-processing)  
27 |   - [Large Scale Data Processing](./data_preparation.md#large-scale-data-processing)   
28 |   - [Data Labeling](./data_preparation.md#data-labeling)
29 | 
30 | * [**Building ML Models**](building_ml_models.md) - Contains resources for running notebooks and training models
31 |   - [SDKs and Infrastructure-as-code](./building_ml_models.md#sdks--infrastructure-as-code)
32 |   - [Training](./building_ml_models.md#training)
33 | 
34 | * [**Deploying ML Models**](deploying_ml_models.md) - Different ways to deploy models and their best practices
35 |   - [Inference](./deploying_ml_models.md#inference)
36 |   - [Hardware Acceleration](./deploying_ml_models.md#hardware-acceleration)
37 |   - [Edge Deployments](./deploying_ml_models.md#edge-deployments)
38 |   - [Debugging](./deploying_ml_models.md#debugging)  
39 | 
40 | * [**MLOps**](mlops.md) - Machine Learning Operations
41 |   - [MLOps Foundations](./mlops.md#mlops-foundations)
42 |   - [SageMaker Pipelines](./mlops.md#sagemaker-pipelines)
43 |   - [Third-Party](./mlops.md#using-third-party) 
44 |   - [Experiment Tracking and Model Registry](./mlops.md#experiment-tracking--model-registry)
45 |   - [Data Versioning and Feature store](./mlops.md#data-versioning--feature-store)
46 |   - [Model Monitoring](./mlops.md#model-monitoring)
47 | 
48 | * [**Low Code / No Code ML**](low_code_no_code_ml.md) - Low code approach to date preparation and model building
49 |   - [Low Code - No Code](./low_code_no_code_ml.md#low-code-no-code)
50 |   - [AutoML](./low_code_no_code_ml.md#automl)
51 |   - [Data Wrangler](./low_code_no_code_ml.md#data-wrangler)
52 | 
53 | * [**Generative AI**](generative_ai.md) - deploy and use generative AI models
54 |   - [Train and deploy Foundational Models](./generative_ai.md#train-and-deploy-foundational-models)
55 |   - [prompt engineering and few shot/zero shot learning](./generative_ai.md#prompt-engineering-and-few-shotzero-shot-learning)
56 |   - [Fine tune Foundational Models](https://github.com/aws-samples/awesome-sagemaker/blob/main/generative_ai.md#fine-tune-foundational-models)
57 |   - [Building Generative AI applications](./generative_ai.md#building-generative-ai-applications)
58 | 
59 | * [**ML Domains**](ml_domains.md) - Deep dive on domains such as NLP, CV, Tabular, Audio and Reinforcement Learning
60 |   - [Responsible AI](./ml_domains.md#responsible-ai)
61 |   - [ML Governance](./ml_domains.md#ml-governance) ([Model Management](./ml_domains.md#model-management), [Security](./ml_domains.md#security), [Cost Tracking & Control](./ml_domains.md#cost-tracking--control))
62 |   - [Computer Vision](./ml_domains.md#computer-vision)
63 |   - [Natural Language Processing](./ml_domains.md#natural-language-processing)
64 |   - [R](./ml_domains.md#r)
65 |   - [Audio](./ml_domains.md#audio)
66 | 
67 | * [**Learning Sagemaker**](learning_sagemaker.md) - Trainings, certifications, books and community
68 |   - [Certification](learning_sagemaker.md#certification)
69 |   - [MOOCs](learning_sagemaker.md#moocs)
70 |   - [Digital & Classroom](learning_sagemaker.md#digital--classroom)
71 |   - [Tutorials](learning_sagemaker.md#tutorials)
72 |   - [Community](learning_sagemaker.md#community)
73 |   - [Books](learning_sagemaker.md#books)
74 |   - [News](learning_sagemaker.md#news)
75 | 
76 | ## :handshake: Contributing
77 | 
78 | If you'd like to open an issue, for having a defunct link removed or corrected, or you want to propose interesting content and share it into the list through a pull request, please read our [contributing guidelines](./CONTRIBUTING.md).
79 | The pull request will be evaluated by the project owners and incorporated into the list. Please ensure that you add the link to the appropriate sub-page and the link points to unique content that is not already covered by one of the other links.
80 | We're extremely excited to receive contributions from the community, and we're still working on the best mechanism to take in examples from external sources.
81 | 


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/generative_ai.md:
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 1 | <div align="center">
 2 |   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Faws-samples%2Fawesome-sagemaker%2Fblob%2Fmain%2Fgenerative_ai.md&count_bg=%23198ED5&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="hits">
 3 | </div>
 4 | 
 5 | ## Generative AI
 6 | 
 7 | ### Train and deploy Foundational Models
 8 | - [Blog] [Fine-tune Llama 2 using QLoRA and Deploy it on Amazon SageMaker with AWS Inferentia2](https://aws.amazon.com/blogs/machine-learning/fine-tune-llama-2-using-qlora-and-deploy-it-on-amazon-sagemaker-with-aws-inferentia2/)
 9 | - [Blog] [Create a web UI to interact with LLMs using Amazon SageMaker JumpStart](https://aws.amazon.com/blogs/machine-learning/create-a-web-ui-to-interact-with-llms-using-amazon-sagemaker-jumpstart/)
10 | - [Blog] [Enable faster training with Amazon SageMaker data parallel library](https://aws.amazon.com/blogs/machine-learning/enable-faster-training-with-amazon-sagemaker-data-parallel-library/)
11 | - [Code] [JumpStart Foundation Models Examples](https://github.com/aws/amazon-sagemaker-examples/tree/main/introduction_to_amazon_algorithms/jumpstart-foundation-models)
12 | - [Blog] [Deploy large models at high performance using FasterTransformer on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/deploy-large-models-at-high-performance-using-fastertransformer-on-amazon-sagemaker/)
13 | - [Blog] [Training large language models on Amazon SageMaker: Best practices](https://aws.amazon.com/blogs/machine-learning/training-large-language-models-on-amazon-sagemaker-best-practices/)
14 | - [Workshop] [Large Generative AI model hosting workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/bb62b5d7-313f-4733-88cd-9c1aa41c724d/en-US)
15 | - [Code] [Deployments for generative AI workshop](https://github.com/aws/amazon-sagemaker-examples/tree/main/inference/generativeai/llm-workshop)
16 | - [Documentation] [Deep learning containers for large model inference](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints-large-model-dlc.html)
17 | - [Blog] [External] [Create Your Own Large Language Model Playground in SageMaker Studio](https://towardsdatascience.com/create-your-own-large-language-model-playground-in-sagemaker-studio-1be5846c5089)
18 | - [Code] [Deploy and use BloomZ 7b1](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text2text-generation-bloomz.ipynb)
19 | - [Code] [Deploy and use FLAN T5 models ](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text2text-generation-flan-t5.ipynb)
20 | - [Code] [Deploy InstructPix2Pix](https://github.com/aws/amazon-sagemaker-examples/tree/main/advanced_functionality/huggingface_deploy_instructpix2pix)
21 | - [Code] [Serve gpt-j-6B on SageMaker with DJLServing](https://github.com/aws/amazon-sagemaker-examples/blob/main/inference/generativeai/deepspeed/GPT-J-6B_DJLServing_with_PySDK.ipynb)
22 | - [Code] [Deploy Falcon 7B and 40B on SageMaker](https://github.com/aws/amazon-sagemaker-examples/tree/main/inference/generativeai/llm-workshop/lab10-falcon-40b-and-7b), [Falcon 7B and 40B using Jumpstart](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-falcon.ipynb)
23 | - [Code] [Deploy OpenLLama on SageMaker](https://github.com/aws/amazon-sagemaker-examples/blob/main/inference/generativeai/llm-workshop/lab10-open-llama/open-llama-7b/open_llama_7b.ipynb) , [Open LLama using Jumpstart](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-open-llama.ipynb)
24 | - [Code] [Deploy Stable Diffusion on Inferentia2](https://github.com/aws/amazon-sagemaker-examples/blob/main/inference/generativeai/llm-workshop/lab9-inf2-stable-diffusion/SageMaker-SD21-INF2.ipynb)
25 | - [Code] [Deploy Dolly-12B on SageMaker](https://github.com/aws/amazon-sagemaker-examples/blob/main/inference/generativeai/llm-workshop/deploy-dolly-12b/dolly-12b-deepspeed-sagemaker.ipynb)
26 | 
27 | 
28 | 
29 | ### prompt engineering and few shot/zero shot learning
30 | - [Blog] [Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart](https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligence/generative-ai/)
31 | - [Code] [GPT-J, GPT-Neo Few-shot learning](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-few-shot-learning.ipynb)
32 | 
33 | ### Fine tune Foundational Models
34 | - [Blog] [Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data](https://aws.amazon.com/blogs/machine-learning/domain-adaptation-fine-tuning-of-foundation-models-in-amazon-sagemaker-jumpstart-on-financial-data/)
35 | - [Blog] [Fine-tune text-to-image Stable Diffusion models with Amazon SageMaker JumpStart](https://aws.amazon.com/blogs/machine-learning/fine-tune-text-to-image-stable-diffusion-models-with-amazon-sagemaker-jumpstart/)
36 | - [Code] [External] [Jumpstart Generative AI Examples](https://github.com/arunprsh/sagemaker-jumpstart-generative-ai-examples)
37 | - [Code] [Fine-tuning text generation GPT-J 6B model on domain specific dataset](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/domain-adaption-finetuning-gpt-j-6b.ipynb)
38 | 
39 | ### Building Generative AI applications
40 | - [Code] [AWS Generative AI CDK Constructs](https://github.com/awslabs/generative-ai-cdk-constructs)
41 | - [Blog] [Quickly build high-accuracy Generative AI applications on enterprise data using Amazon Kendra, LangChain, and large language models](https://aws.amazon.com/blogs/machine-learning/quickly-build-high-accuracy-generative-ai-applications-on-enterprise-data-using-amazon-kendra-langchain-and-large-language-models/)
42 | - [Blog] [Build custom chatbot applications using OpenChatkit models on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/build-custom-chatbot-applications-using-openchatkit-models-on-amazon-sagemaker/)
43 | - [Blog] [Virtual fashion styling with generative AI using Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/virtual-fashion-styling-with-generative-ai-using-amazon-sagemaker/)
44 | - [Blog] [Architect personalized generative AI SaaS applications on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/architect-personalized-generative-ai-saas-applications-on-amazon-sagemaker/)
45 | - [Workshop] [Language Model Workshop for Financial Services](https://github.com/aws-samples/large-model-workshop-financial-services)
46 | - [Blog] [Inpaint images with Stable Diffusion using Amazon SageMaker JumpStart](https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligence/generative-ai/)
47 | - [Blog] [Run text generation with Bloom and GPT models on Amazon SageMaker JumpStart](https://aws.amazon.com/blogs/machine-learning/run-text-generation-with-gpt-and-bloom-models-on-amazon-sagemaker-jumpstart/)
48 | 
49 | 


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