# Resources
---

This is a list of SageMaker-related resources.

## General SageMaker documentation and blog posts
- [R1]: [Amazon SageMaker Pipelines documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html)
- [R2]: [Best practices for multi-account AWS environment](https://aws.amazon.com/organizations/getting-started/best-practices/)
- [R3]: [AWS Well-Architected Framework - Machine Learning Lens Whitepaper](https://d1.awsstatic.com/whitepapers/architecture/wellarchitected-Machine-Learning-Lens.pdf)  
- [R4]: [Terraform provider AWS GitHub](https://github.com/hashicorp/terraform-provider-aws)
- [R5]: [Data processing options for AI/ML](https://aws.amazon.com/blogs/machine-learning/data-processing-options-for-ai-ml/)
- [R6]: [Architect and build the full machine learning lifecycle with AWS: An end-to-end Amazon SageMaker demo](https://aws.amazon.com/blogs/machine-learning/architect-and-build-the-full-machine-learning-lifecycle-with-amazon-sagemaker/)
- [R7]: [End-to-end Amazon SageMaker demo](https://github.com/aws/amazon-sagemaker-examples/tree/master/end_to_end)
- [R8]: [Multi-account model deployment with Amazon SageMaker Pipelines](https://aws.amazon.com/blogs/machine-learning/multi-account-model-deployment-with-amazon-sagemaker-pipelines/)
- [R9]: [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/)
- [R10]: [Best Practices for Organizational Units with AWS Organizations](https://aws.amazon.com/blogs/mt/best-practices-for-organizational-units-with-aws-organizations/)
- [R11]: [Build a CI/CD pipeline for deploying custom machine learning models using AWS services](https://aws.amazon.com/blogs/machine-learning/build-a-ci-cd-pipeline-for-deploying-custom-machine-learning-models-using-aws-services/)
- [R12]: [Configuring Amazon SageMaker Studio for teams and groups with complete resource isolation](https://aws.amazon.com/fr/blogs/machine-learning/configuring-amazon-sagemaker-studio-for-teams-and-groups-with-complete-resource-isolation/)
- [R13]: [Enable feature reuse across accounts and teams using Amazon SageMaker Feature Store](https://aws.amazon.com/blogs/machine-learning/enable-feature-reuse-across-accounts-and-teams-using-amazon-sagemaker-feature-store/)
- [R14]: [How Genworth built a serverless ML pipeline on AWS using Amazon SageMaker and AWS Glue](https://aws.amazon.com/blogs/machine-learning/how-genworth-built-a-serverless-ml-pipeline-on-aws-using-amazon-sagemaker-and-aws-glue/)
- [R15]: [SageMaker cross-account model](https://aws.amazon.com/premiumsupport/knowledge-center/sagemaker-cross-account-model/)
- [R16]: [Use Amazon CloudWatch custom metrics for real-time monitoring of Amazon Sagemaker model performance](https://aws.amazon.com/blogs/machine-learning/use-amazon-cloudwatch-custom-metrics-for-real-time-monitoring-of-amazon-sagemaker-model-performance/)
- [R17]: [Automate feature engineering pipelines with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/automate-feature-engineering-pipelines-with-amazon-sagemaker/)
- [R18]: [Build a Secure Enterprise Machine Learning Platform on AWS](https://docs.aws.amazon.com/whitepapers/latest/build-secure-enterprise-ml-platform/build-secure-enterprise-ml-platform.html)
- [R19]: [Automate Amazon SageMaker Studio setup using AWS CDK](https://aws.amazon.com/blogs/machine-learning/automate-amazon-sagemaker-studio-setup-using-aws-cdk/)
- [R20]: [How to use trust policies with IAM roles](https://aws.amazon.com/blogs/security/how-to-use-trust-policies-with-iam-roles/)
- [R21]: [Use Amazon SageMaker Studio Notebooks](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks.html)
- [R22]: [Shutting Down Amazon SageMaker Studio Apps on a Scheduled Basis](https://medium.com/swlh/shutting-down-amazon-sagemaker-studio-kernelgateways-automatically-with-aws-lambda-41e93afef06b)
- [R23]: [Register and Deploy Models with Model Registry](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html)
- [R24]: [Setting up secure, well-governed machine learning environments on AWS](https://aws.amazon.com/blogs/mt/setting-up-machine-learning-environments-aws/)
- [R25]: [Machine Learning Best Practices in Financial Services: Blog post](https://aws.amazon.com/blogs/machine-learning/machine-learning-best-practices-in-financial-services/)
- [R26]: [Machine Learning Best Practices in Financial Services: Whitepaper](https://d1.awsstatic.com/whitepapers/machine-learning-in-financial-services-on-aws.pdf)
- [R27]: [Dynamic A/B testing for machine learning models with Amazon SageMaker MLOps projects](https://aws.amazon.com/blogs/machine-learning/dynamic-a-b-testing-for-machine-learning-models-with-amazon-sagemaker-mlops-projects/)
- [R28]: [Hosting a private PyPI server for Amazon SageMaker Studio notebooks in a VPC](https://aws.amazon.com/blogs/machine-learning/hosting-a-private-pypi-server-for-amazon-sagemaker-studio-notebooks-in-a-vpc/)
- [R29]: [Automate a centralized deployment of Amazon SageMaker Studio with AWS Service Catalog](https://aws.amazon.com/blogs/machine-learning/automate-a-centralized-deployment-of-amazon-sagemaker-studio-with-aws-service-catalog/)
- [R30]: [Orchestrate XGBoost ML Pipelines with Amazon Managed Workflows for Apache Airflow](https://aws.amazon.com/blogs/machine-learning/orchestrate-xgboost-ml-pipelines-with-amazon-managed-workflows-for-apache-airflow/)
- [R31]: [Amazon SageMaker Identity-Based Policy Examples](https://docs.aws.amazon.com/sagemaker/latest/dg/security_iam_id-based-policy-examples.html)
- [R32]: [Connect to SageMaker Through a VPC Interface Endpoint](https://docs.aws.amazon.com/sagemaker/latest/dg/interface-vpc-endpoint.html)
- [R33]: [Extend Amazon SageMaker Pipelines to include custom steps using callback steps](https://aws.amazon.com/blogs/machine-learning/extend-amazon-sagemaker-pipelines-to-include-custom-steps-using-callback-steps/)
- [R34]: [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/)
- [R35]: [Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language](https://aws.amazon.com/blogs/machine-learning/define-and-run-machine-learning-pipelines-on-step-functions-using-python-workflow-studio-or-states-language/)
- [R36]: [Dive deep into Amazon SageMaker Studio Notebooks architecture](https://aws.amazon.com/blogs/machine-learning/dive-deep-into-amazon-sagemaker-studio-notebook-architecture/)
- [R37]: [Model and data lineage in machine learning experimentation](https://aws.amazon.com/blogs/machine-learning/model-and-data-lineage-in-machine-learning-experimentation/)
- [R38]: [Customize Amazon SageMaker Studio using Lifecycle Configurations](https://aws.amazon.com/blogs/machine-learning/customize-amazon-sagemaker-studio-using-lifecycle-configurations/)
- [R39]: [Patterns for multi-account, hub-and-spoke Amazon SageMaker model registry](https://aws.amazon.com/blogs/machine-learning/patterns-for-multi-account-hub-and-spoke-amazon-sagemaker-model-registry/)
- [R40]: [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/)
- [R41]: [Scheduling Jupyter notebooks on SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)
- [R42]: [Building machine learning workflows with Amazon SageMaker Processing jobs and AWS Step Functions](https://aws.amazon.com/blogs/machine-learning/building-machine-learning-workflows-with-amazon-sagemaker-processing-jobs-and-aws-step-functions/) 
- [R43]: [How Slalom and WordStream Used MLOps to Unify Machine Learning and DevOps on AWS](https://aws.amazon.com/blogs/apn/how-slalom-and-wordstream-used-mlops-to-unify-machine-learning-and-devops-on-aws/)
- [R44]: [Create a cross-account machine learning training and deployment environment with AWS Code Pipeline](https://aws.amazon.com/blogs/machine-learning/create-a-cross-account-machine-learning-training-and-deployment-environment-with-aws-code-pipeline/)
- [R45]: [Amazon SageMaker now supports cross-account lineage tracking and multi-hop lineage querying](https://aws.amazon.com/about-aws/whats-new/2021/12/amazon-sagemaker-cross-account-lineage-tracking-query/)
- [R46]: [MLflow Open Machine Learning Platform on AWS](https://github.com/aws-samples/aws-mlflow-sagemaker-cdk)
- [R47]: [MLOps Platforms by Thoughtworks: github repo](https://github.com/thoughtworks/mlops-platforms)
- [R48]: [MLOps: Continuous Delivery for Machine Learning on AWS](https://d1.awsstatic.com/whitepapers/mlops-continuous-delivery-machine-learning-on-aws.pdf)
- [R49]: [Blog post series: How NatWest Group built a scalable, secure, and sustainable MLOps platform](https://aws.amazon.com/blogs/machine-learning/part-1-how-natwest-group-built-a-scalable-secure-and-sustainable-mlops-platform/)
- [R41]: [Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines](https://aws.amazon.com/blogs/machine-learning/organize-your-machine-learning-journey-with-amazon-sagemaker-experiments-and-amazon-sagemaker-pipelines/)

## AWS Solutions
- [SOL1]: [AWS MLOps Framework](https://aws.amazon.com/solutions/implementations/aws-mlops-framework/)
- [SOL2]: [Amazon SageMaker with Guardrails on AWS](https://aws.amazon.com/quickstart/architecture/amazon-sagemaker-with-guardrails/)

## Secure ML environments
- [S1]: [Building secure machine learning environments with Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/building-secure-machine-learning-environments-with-amazon-sagemaker/)
- [S2]: [Secure data science Reference Architecture GitHub](https://github.com/aws-samples/secure-data-science-reference-architecture)
- [S3]: [SageMaker Notebook instance lifecycle config samples GitHub](https://github.com/aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples)
- [S4]: [Securing Amazon SageMaker Studio connectivity using a private VPC](https://aws.amazon.com/blogs/machine-learning/securing-amazon-sagemaker-studio-connectivity-using-a-private-vpc/)
- [S5]: [Secure deployment of Amazon SageMaker resources](https://aws.amazon.com/blogs/security/secure-deployment-of-amazon-sagemaker-resources/)
- [S6]: [Understanding Amazon SageMaker notebook instance networking configurations and advanced routing options](https://aws.amazon.com/blogs/machine-learning/understanding-amazon-sagemaker-notebook-instance-networking-configurations-and-advanced-routing-options/)
- [S7]: [Security group rules for different use cases](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/security-group-rules-reference.html)
- [S8]: [Data encryption at rest in SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest-studio.html)
- [S9]: [Connect SageMaker Studio Notebooks to Resources in a VPC](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-notebooks-and-internet-access.html)
- [S10]: [Control root access to Amazon SageMaker notebook instances](https://aws.amazon.com/blogs/machine-learning/control-root-access-to-amazon-sagemaker-notebook-instances/)  
- [S11]: [7 ways to improve security of your machine learning workflows](https://aws.amazon.com/blogs/security/7-ways-to-improve-security-of-your-machine-learning-workflows/)
- [S12]: [PySparkProcessor - Unable to locate credentials for boto3 call in AppMaster](https://github.com/aws/amazon-sagemaker-examples/issues/1689)
- [S13]: [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/)
- [S14]: [Securing Amazon SageMaker Studio internet traffic using AWS Network Firewall](https://aws.amazon.com/blogs/machine-learning/securing-amazon-sagemaker-studio-internet-traffic-using-aws-network-firewall/)
- [S15]: [Secure Your SageMaker Studio Access Using AWS PrivateLink and AWS IAM SourceIP Restrictions](https://aws.amazon.com/about-aws/whats-new/2020/12/secure-sagemaker-studio-access-using-aws-privatelink-aws-iam-sourceip-restrictions/)
- [S16]: [Model Risk Management by Deloitte](https://www2.deloitte.com/content/dam/Deloitte/fr/Documents/risk/deloitte_model-risk-management_plaquette.pdf)
- [S17]: [Building secure Amazon SageMaker access URLs with AWS Service Catalog](https://aws.amazon.com/blogs/mt/building-secure-amazon-sagemaker-access-urls-with-aws-service-catalog/)
- [S18]: [Secure multi-account model deployment with Amazon SageMaker Series](https://aws.amazon.com/blogs/machine-learning/part-1-secure-multi-account-model-deployment-with-amazon-sagemaker/)
- [S19]: [Launch Amazon SageMaker Studio from external applications using presigned URLs](https://aws.amazon.com/blogs/machine-learning/launch-amazon-sagemaker-studio-from-external-applications-using-presigned-urls/)
- [S20]: [Organizing Your AWS Environment Using Multiple Accounts](https://docs.aws.amazon.com/whitepapers/latest/organizing-your-aws-environment/organizing-your-aws-environment.html)

## Workshops
- [W1]: [SageMaker immersion day GitHub](https://github.com/aws-samples/amazon-sagemaker-immersion-day)  
- [W2]: [SageMaker immersion day workshop 2.0](https://sagemaker-immersionday.workshop.aws/)  
- [W3]: [Amazon Sagemaker MLOps workshop GitHub](https://github.com/awslabs/amazon-sagemaker-mlops-workshop)
- [W4]: [Operationalizing the Machine Learning Pipeline](https://operational-machine-learning-pipeline.workshop.aws/)
- [W5]: [Safe MLOps deployment pipeline](https://mlops-safe-deployment-pipeline.workshop.aws/)
- [W6]: [Building secure environments workshop](https://sagemaker-workshop.com/security_for_sysops.html)
- [W7]: [Amazon Managed Workflows for Apache Airflow workshop](https://amazon-mwaa-for-analytics.workshop.aws/en/)
- [W8]: [Secure data science with Amazon SageMaker Studio Workshop](https://catalog.us-east-1.prod.workshops.aws/v2/workshops/c882cd42-8ec8-4112-9469-9fab33471e85/en-US)
- [W9]: [MLOps and Integrations](https://mlops-and-integrations.workshop.aws/)
- [W10]: [Serverless ML pipeline](https://github.com/dylan-tong-aws/aws-serverless-ml-pipeline)
- [W11]: [Basic SageMaker MLOps](https://github.com/aws-samples/mlops-amazon-sagemaker-devops-with-ml)
- [W12]: [data science on AWS (ML end-to-end pipeline)](https://github.com/data-science-on-aws/workshop)
- [W13]: [Amazon SageMaker End to End Workshop](https://github.com/aws-samples/sagemaker-end-to-end-workshop)
- [W14]: [End to end Machine Learning with Amazon SageMaker](https://github.com/aws-samples/amazon-sagemaker-build-train-deploy)

## MLOps and ML production related resources
- [MLOps for Enterprises](https://github.com/aws-samples/sagemaker-custom-project-templates/tree/main/mlops-multi-account-cdk)
- [Awesome MLOps](https://github.com/visenger/awesome-mlops)
- [Awesome production machine learning](https://github.com/EthicalML/awesome-production-machine-learning)
- [A Guide to Production Level Deep Learning](https://github.com/alirezadir/Production-Level-Deep-Learning)
- [Feature Stores for ML](https://www.featurestore.org/)
- [Introducing TWIML’s New ML and AI Solutions Guide](https://twimlai.com/solutions/introducing-twiml-ml-ai-solutions-guide/)
- [TWIML podcast: Feature Stores for MLOps with Mike del Balso](https://twimlai.com/feature-stores-for-mlops-with-mike-del-balso/)
- [TWIML podcast: Enterprise Readiness, MLOps and Lifecycle Management with - - Jordan Edwards](https://twimlai.com/twiml-talk-321-enterprise-readiness-mlops-and-lifecycle-management-with-jordan-edwards/)
- [Full stack deep learning free online course](https://course.fullstackdeeplearning.com/)
- [Continuous Delivery for Machine Learning](https://martinfowler.com/articles/cd4ml.html)
- [Feature Store vs Data Warehouse](https://medium.com/data-for-ai/feature-store-vs-data-warehouse-306d1567c100)
- [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/)
- [MLflow and PyTorch — Where Cutting Edge AI meets MLOps](https://medium.com/pytorch/mlflow-and-pytorch-where-cutting-edge-ai-meets-mlops-1985cf8aa789)
- [5 Lessons Learned Building an Open Source MLOps Platform](https://towardsdatascience.com/5-lessons-learned-building-an-open-source-mlops-platform-624574a44c09)

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