AI Engineer Portal

Career transition, organized like a product.

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Focus the portal on Python depth, shipping projects, evaluation, and portfolio readiness.

Phase 1 MVP

Build career momentum with visible, repeatable progress.

Single-user private mode

Learning Path

AI Deployment and MLOps

Make AI systems operable beyond the first successful demo.

0% complete

advanced · 18 hours

Lesson 1

Deployment shapes for AI products

Compare monolith APIs, workers, queues, and evaluation jobs.

Lesson 2

Secrets, environments, and provider configuration

Manage credentials, feature flags, and environment drift safely.

Lesson 3

Caching and throughput management

Use caching where it reduces cost without hiding bugs.

Lesson 4

Scheduled jobs and background processing

Separate user-facing APIs from indexing, evaluation, and ingestion work.

Lesson 5

Portfolio slice: deployable AI service

Show that your project survives outside localhost.