Managing Secrets in Databricks and Azure Key Vault for MLOps

Securing access keys, database credentials, and API tokens is a fundamental requirement when building reliable machine learning workflows. This written course provides a step-by-step path to understanding how secrets management operates within cloud data environments and automated pipelines. Through clear written explanations and practical code examples, you will explore how Databricks secret scopes work alongside Azure Key Vault to safeguard sensitive information throughout the machine learning lifecycle. What you'll learn: - Understand foundational security principles and terminology for modern cloud workflows and MLOps - Configure Azure Key Vault instances to store and protect sensitive credentials - Create and manage both Databricks-backed and Azure Key Vault-backed secret scopes - Retrieve secrets securely within Databricks notebooks and data jobs using standard utilities - Apply access control lists and role-based permissions to maintain least-privilege access - Integrate secret retrieval patterns into automated machine learning pipelines The material begins with foundational concepts, key terminology, and security architectures before advancing through step-by-step configuration guides, code patterns, and written review exercises. This course is designed for beginners in cloud data security, data engineers, and machine learning practitioners who want to protect sensitive credentials in their analytics projects without needing prior security experience. Start reading today to build secure and professional data workflows.