9 docs tagged with "engineering"

Build production-grade AI agents - from MCP and tool use to multi-agent systems and long-horizon task completion.

The complete engineering track for building, shipping, and operating production AI systems - LLMOps, observability, gateways, synthetic data, compression, and security.

Design and build production ML systems - model serving, real-time inference, vector databases, GPU infrastructure, cost optimization, and platform engineering.

The data infrastructure foundation for AI/ML systems - batch and stream processing, feature stores, data lakehouse, pipeline orchestration, and real-time feature engineering.

A structured, production-grade LLM curriculum - from transformer architecture to alignment and safety. 17 modules covering every layer of the LLM stack.

A structured, production-grade Machine Learning curriculum - from the math that matters to models that deploy. Built for engineers who want to understand how ML works, not just how to call an API.

A structured, production-grade Python curriculum - from fundamentals to enterprise architecture. Built for engineers who want to understand how Python works, not just how to use it.

A structured, production-grade MLOps curriculum - experiment tracking, CI/CD for ML, Kubernetes, monitoring, LLMOps, infrastructure as code, and cost management.

Master Python data structures at the engineering level - lists, dicts, sets, tuples, heaps, deques, Counter, defaultdict, time complexity, sorting, and selection strategy. The most comprehensive data structures module for Python engineers.