AI Architect

A course on AI system design interviews, from classic ML systems such as recommendations, ranking and fraud detection to LLM serving costs, RAG, generative AI products and agentic systems, with a repeatable framework and full case studies. It takes about 20 hours across 9 chapters. It suits engineers who can write code and know what a model, an API and a database are, and are facing design rounds that now include AI.

• AI
• System design
• Job search

A beginner course that gives software engineers and students a clear, practical grounding in modern AI: core concepts, training a simple model, neural networks and deep learning, search algorithms, reinforcement learning, agentic AI, modern tools and prompting, and the ethics and hype around it all. It takes about five hours across 14 chapters with playgrounds and assessments, and needs no prior AI background.

• AI
• Machine learning

A course on the named patterns that make microservice architectures workable: strangler fig, API gateway, backends for frontends, service discovery, circuit breaker, bulkhead, retry, sidecar, service mesh, database per service, saga, transactional outbox, event-driven architecture, CQRS, event sourcing and externalised configuration. Each pattern starts with the problem it solves and the cost it adds, with short Java examples. It suits developers and aspiring architects working with, or moving towards, service-based systems.

• System design
• Backend
• DevOps

A course on the judgment behind microservices in a system design round: when to choose them over a monolith, how to draw service boundaries, how services communicate, how to handle failure and keep data consistent, and how microservices run in production. It ends with full interview scenarios and a rapid-review question bank. Every lesson closes with a speakable 30-second answer and likely follow-ups. It suits engineers who need to discuss microservice trade-offs convincingly.

• System design
• Backend
• DevOps

A six-stage path into paid remote AI data work, with a version for data annotation and one for AI trainers. It covers why models need people, the honest picture of pay, project gaps and scams, the guideline and quality skills platforms test, getting accepted, getting paid and keeping records, and moving up into reviewer, evals, data or ML roles. It suits students and graduates who want flexible remote income and read instructions carefully.

• Career
• AI
• Remote work
• Job search

The official docs for Pydantic, Python's most widely used data validation library: models declared with type hints, strict and lax validation, JSON Schema generation and serialisation. It is how Python agents validate model output and tool arguments, and most agent frameworks accept Pydantic models directly.

• Programming
• Backend
• AI