AI Architect Course: From Fundamentals to Production

Understand Transformer architecture from residual streams and attention masks to encoder, decoder, positional signals, KV-cache costs, and validation tests.

Understand attention mechanisms from QKV tensor shapes to causal masks, multi-head attention, numerical stability, inference trade-offs, and reliable testing.

A rigorous introduction to neural networks, automatic differentiation, optimization, CNNs, sequence models, Transformers, generative models, data leakage, regularization, and reproducible evaluation. The guide separates illustrative equations from production decisions and avoids unsupported performance claims.

Learn how neural networks combine affine layers, activations, losses, and backpropagation. Includes runnable Go code, tests, and evaluation boundaries.

Learn how vector embeddings power search and RAG, then choose models, metrics, dimensions, indexes, evaluation datasets, hybrid retrieval, and migration plans.

Learn how generative AI works across text, images, audio, video, and code, then design evaluation, provenance, safety controls, release gates, and rollback.

Understand NLP tasks, tokenization, BERT and generative models, with practical guidance on labels, data splits, entity offsets, and classification metrics in Go.

Understand diffusion models from the forward noising process to learned denoising, DDPM/DDIM sampling, latent diffusion, conditioning, and deployment trade-offs. This guide separates equations from version-sensitive Diffusers code and covers reproducibility, safety, licensing, quality, latency, and cost evaluation.

Learn LLM inference from chat templates and tokenization through queueing, prefill, decode, streaming, SLO metrics, capacity planning, and rollout gates.

Learn how Mixture of Experts routes tokens through sparse expert layers, why active parameters do not predict speed, and how to evaluate load balance, communication, memory, quality, and serving performance.

Understand reasoning models through OpenAI o1 and DeepSeek R1: public training evidence, test-time compute, verifier limits, evaluation design, and production routing.

Understand Mamba and state space models from recurrence and selective scan through Mamba-2 SSD, Mamba-3, hybrid designs, benchmarks, and deployment trade-offs.

Engineer reasoning effort without treating thinking controls as one standard API. Compare provider contracts, hidden-token accounting, truncation, routing errors, matched-budget evaluation, tool loops, and cost per verified task with a runnable Go policy gate.

Claude 4 introduced hybrid reasoning, stronger coding agents, and a fivefold launch-price gap between Opus 4 and Sonnet 4. Learn what its benchmark scores actually measured, why the seven-hour coding claim was not an SLA, how to evaluate model tiers on your own tasks, and how to migrate retired Claude 4 API workloads safely.

A practical, version-aware four-layer model for AI context: instructions, knowledge, memory, and orchestration. Learn how to set budgets, route retrieval, compact memory, validate tool output, and measure quality without treating token ratios or model behavior as universal facts.

A production guide to Mixture of Agents architecture: original MoA versus Self-MoA, proposer diversity, synthesis failure modes, versioned evaluation, cost and latency controls, and a compilable Go orchestration pattern with evidence-aware release gates.

Test-time compute engineering explained through reasoning budgets, self-consistency, search, verifier costs, and a runnable Go controller with explicit stopping rules.

Design an LLM Gateway with explicit protocol contracts, tenant quotas, safe streaming retries, budget reconciliation, private telemetry, and config rollback.