Description

Introduction

Secure and Compliant ML Workflows in AWS SageMaker is a practical training course designed to help ML practitioners and data scientists develop, deploy, and manage machine learning models while adhering to strict security and compliance standards. It covers AWS security best practices, identity and access control, data encryption, network isolation, auditability, and compliance frameworks like HIPAA, GDPR, and FedRAMP, all within the context of SageMaker’s managed infrastructure.

Prerequisites

Participants should have:

• Basic knowledge of machine learning concepts and SageMaker workflows

• Familiarity with AWS services such as IAM, VPC, and CloudTrail

• Understanding of security principles and compliance requirements (e.g., PII, HIPAA)

• Experience using Python and Jupyter notebooks

Table of Contents

1. Overview of Security and Compliance in ML
    1.1 Why Security and Compliance Matter in ML Workflows
    1.2 Shared Responsibility Model in AWS
    1.3 Overview of AWS Compliance Programs (HIPAA, GDPR, SOC 2, etc.)

2. Identity and Access Management (IAM)
    2.1 IAM Roles and Policies for SageMaker
    2.2 Least Privilege and Fine-Grained Permissions
    2.3 Role-Based Access for Teams and Projects

3. Data Protection and Encryption
    3.1 Encrypting Data at Rest and in Transit
    3.2 KMS Integration with SageMaker
    3.3 Handling Sensitive Data (e.g., PII, PHI)

4. Network Isolation and Secure Environments
    4.1 Using VPC Endpoints for SageMaker
    4.2 PrivateLink and No-Internet Access Configuration
    4.3 Secure Training and Inference Environments

5. Secure Model Training and Deployment
    5.1 Protecting Training Jobs with IAM and Encryption
    5.2 Using Model Registry with Approval Workflows
    5.3 Endpoint Security and Model Access Control

6. Monitoring, Auditing, and Logging
    6.1 CloudTrail and CloudWatch Integration
    6.2 Monitoring ML Models for Drift and Anomalies
    6.3 Generating Audit Trails for Compliance Reporting

7. Compliance-Ready ML Architecture
    7.1 Designing for HIPAA and GDPR Compliance
    7.2 Data Residency and Sovereignty Considerations
    7.3 SageMaker in Regulated Industries (Healthcare, Finance, Government)

8. Real-World Use Case: Healthcare ML with HIPAA Compliance
    8.1 Problem Definition and Risk Mitigation
    8.2 Secure Data Pipeline Design
    8.3 Deployment, Monitoring, and Compliance Audit

Security and compliance are critical to operationalizing machine learning in regulated environments. AWS SageMaker offers robust tools and integrations to help you build ML workflows that meet the highest standards for data protection, access control, and regulatory compliance. By following best practices taught in this course, you can confidently scale your ML initiatives without compromising trust or governance.