resources.md

Resources

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This is a list of SageMaker-related resources.

## General SageMaker documentation and blog posts

• [R1]: Amazon SageMaker Pipelines documentation
• [R2]: Best practices for multi-account AWS environment
• [R3]: AWS Well-Architected Framework - Machine Learning Lens Whitepaper
• [R4]: Terraform provider AWS GitHub
• [R5]: Data processing options for AI/ML
• [R6]: Architect and build the full machine learning lifecycle with AWS: An end-to-end Amazon SageMaker demo
• [R7]: End-to-end Amazon SageMaker demo
• [R8]: Multi-account model deployment with Amazon SageMaker Pipelines
• [R9]: Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines
• [R10]: Best Practices for Organizational Units with AWS Organizations
• [R11]: 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
• [R13]: 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
• [R15]: SageMaker cross-account model
• [R16]: Use Amazon CloudWatch custom metrics for real-time monitoring of Amazon Sagemaker model performance
• [R17]: Automate feature engineering pipelines with Amazon SageMaker
• [R18]: Build a Secure Enterprise Machine Learning Platform on AWS
• [R19]: Automate Amazon SageMaker Studio setup using AWS CDK
• [R20]: How to use trust policies with IAM roles
• [R21]: Use Amazon SageMaker Studio Notebooks
• [R22]: Shutting Down Amazon SageMaker Studio Apps on a Scheduled Basis
• [R23]: Register and Deploy Models with Model Registry
• [R24]: Setting up secure, well-governed machine learning environments on AWS
• [R25]: Machine Learning Best Practices in Financial Services: Blog post
• [R26]: Machine Learning Best Practices in Financial Services: Whitepaper
• [R27]: 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
• [R29]: Automate a centralized deployment of Amazon SageMaker Studio with AWS Service Catalog
• [R30]: Orchestrate XGBoost ML Pipelines with Amazon Managed Workflows for Apache Airflow
• [R31]: Amazon SageMaker Identity-Based Policy Examples
• [R32]: Connect to SageMaker Through a VPC Interface Endpoint
• [R33]: Extend Amazon SageMaker Pipelines to include custom steps using callback steps
• [R34]: 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
• [R36]: Dive deep into Amazon SageMaker Studio Notebooks architecture
• [R37]: Model and data lineage in machine learning experimentation
• [R38]: Customize Amazon SageMaker Studio using Lifecycle Configurations
• [R39]: Patterns for multi-account, hub-and-spoke Amazon SageMaker model registry
• [R40]: Managing your machine learning lifecycle with MLflow and Amazon SageMaker
• [R41]: Scheduling Jupyter notebooks on SageMaker ephemeral instances
• [R42]: 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
• [R44]: 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
• [R46]: MLflow Open Machine Learning Platform on AWS
• [R47]: MLOps Platforms by Thoughtworks: github repo
• [R48]: MLOps: Continuous Delivery for Machine Learning on AWS
• [R49]: Blog post series: How NatWest Group built a scalable, secure, and sustainable MLOps platform

AWS Solutions

• [SOL1]: AWS MLOps Framework
• [SOL2]: Amazon SageMaker with Guardrails on AWS

Secure ML environments

• [S1]: Building secure machine learning environments with Amazon SageMaker
• [S2]: Secure data science Reference Architecture GitHub
• [S3]: SageMaker Notebook instance lifecycle config samples GitHub
• [S4]: Securing Amazon SageMaker Studio connectivity using a private VPC
• [S5]: Secure deployment of Amazon SageMaker resources
• [S6]: Understanding Amazon SageMaker notebook instance networking configurations and advanced routing options
• [S7]: Security group rules for different use cases
• [S8]: Data encryption at rest in SageMaker Studio
• [S9]: Connect SageMaker Studio Notebooks to Resources in a VPC
• [S10]: Control root access to Amazon SageMaker notebook instances
• [S11]: 7 ways to improve security of your machine learning workflows
• [S12]: PySparkProcessor - Unable to locate credentials for boto3 call in AppMaster
• [S13]: Private package installation in Amazon SageMaker running in internet-free mode
• [S14]: Securing Amazon SageMaker Studio internet traffic using AWS Network Firewall
• [S15]: Secure Your SageMaker Studio Access Using AWS PrivateLink and AWS IAM SourceIP Restrictions
• [S16]: Model Risk Management by Deloitte
• [S17]: Building secure Amazon SageMaker access URLs with AWS Service Catalog
• [S18]: Secure multi-account model deployment with Amazon SageMaker Series
• [S19]: Launch Amazon SageMaker Studio from external applications using presigned URLs

Workshops

• [W1]: SageMaker immersion day GitHub
• [W2]: SageMaker immersion day workshop 2.0
• [W3]: Amazon Sagemaker MLOps workshop GitHub
• [W4]: Operationalizing the Machine Learning Pipeline
• [W5]: Safe MLOps deployment pipeline
• [W6]: Building secure environments workshop
• [W7]: Amazon Managed Workflows for Apache Airflow workshop
• [W8]: Secure data science with Amazon SageMaker Studio Workshop
• [W9]: MLOps and Integrations
• [W10]: Serverless ML pipeline
• [W11]: Basic SageMaker MLOps
• [W12]: data science on AWS (ML end-to-end pipeline)
• [W13]: Amazon SageMaker End to End Workshop
• [W14]: End to end Machine Learning with Amazon SageMaker

MLOps and ML production related resources

• MLOps for Enterprises
• Awesome MLOps
• Awesome production machine learning
• A Guide to Production Level Deep Learning
• Feature Stores for ML
• Introducing TWIML’s New ML and AI Solutions Guide
• TWIML podcast: Feature Stores for MLOps with Mike del Balso
• TWIML podcast: Enterprise Readiness, MLOps and Lifecycle Management with - - Jordan Edwards
• Full stack deep learning free online course
• Continuous Delivery for Machine Learning
• Feature Store vs Data Warehouse
• Seldon Core
• MLflow and PyTorch — Where Cutting Edge AI meets MLOps
• 5 Lessons Learned Building an Open Source MLOps Platform

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