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When to Use Managed Azure ML

For most teams and projects, Managed Azure ML is the recommended path within our AI Platform. Here’s why:

Default for most projects​

  • Suitable for structured tabular data.
  • It supports the most common AI/ML workloads and offers a fast, scalable, and secure foundation.

Quick and easy start​

  • Projects can get up and running very quickly with minimal setup.
  • The opinionated infrastructure and preconfigured environments eliminate common startup overhead.

Ideal for common ML use cases​

Managed Azure ML is especially effective for use cases involving structured, tabular, or time-series data, such as:

  • Exploratory data analysis
  • Regression and classification
  • Anomaly detection

It's also a great platform for testing and experimentation before production deployment.

Integrated workflow automation​

  • GitHub Actions are built in for automating CI/CD workflows.
  • Enables streamlined training, testing, and deployment pipelines.

Trusted support across all levels​

  • Benefit from proven internal expertise across all tiers of development.
  • Teams can rely on trusted support, guidance, and best practices.

Strong security foundations​

  • Built-in role-based access control (RBAC), network isolation, and managed identities.
  • Provides a well-founded security setup aligned with enterprise standards.

Cost management and optimization​

  • Uses Kubernetes clusters for scalable, cost-optimized compute.
  • Dashboards enable spend tracking and cost monitoring.
  • Reduces required engineering hours and speeds up project delivery.