The AI Engineer’s Production Cookbook & Ops Manual
20 battle-tested recipes, triage decision trees, and production deployment checklists
The operational desk reference for running production AI systems. Features copy-paste implementation recipes, triage decision trees for debugging hallucinations and covariate drift, GPU memory calculation formulas, cost optimization runbooks, and pre-flight launch checklists.
Course Prerequisites
- Applied AI Systems & Machine Learning Engineering or practical machine learning deployment experience.
Part of Academy Track:
What You Will Master
Curriculum Modules (4 Modules)
Explore the structured module breakdown, lesson outcomes, and practical lab exercises.
Module 1: Strategic Foundations & Baseline Modeling
4 Lessons • ~4.9 Study Hours (0.49 CEUs)Welcome to Module 1: Strategic Foundations & Baseline Modeling. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Architectural triage decision tree to select between statsmodels, classical scikit-learn/XGBoost, RAG, PEFT/QLoRA, and agentic tool calling.; Executable recipe for Ordinary Least Squares (OLS) statistical inference, p-value extraction, and condition number auditing with statsmodels.; Baseline recipe for training a classical linear regression model from scratch with train/test splits and R², MAE, and RMSE evaluation..
Decision Tree: What Do I Actually Need?
Architectural triage decision tree to select between statsmodels, classical scikit-learn/XGBoost, RAG, PEFT/QLoRA, and agentic tool calling.
Recipe: Statistical Inference (`statsmodels`)
Executable recipe for Ordinary Least Squares (OLS) statistical inference, p-value extraction, and condition number auditing with statsmodels.
Recipe: Train a Classical Model from Scratch
Baseline recipe for training a classical linear regression model from scratch with train/test splits and R², MAE, and RMSE evaluation.
Recipe: Swap the Algorithm (Linear $ o$ Random Forest $ o$ XGBoost)
Drop-in workflow to benchmark and swap estimators from Linear Regression to Random Forest Regressor and XGBoost with zero data pipeline changes.
Module 2: Classical Machine Learning & Pipeline Engineering
6 Lessons • ~7.3 Study Hours (0.73 CEUs)Welcome to Module 2: Classical Machine Learning & Pipeline Engineering. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Systematic hyperparameter tuning recipe using GridSearchCV and RandomizedSearchCV with cross-validation on Random Forest estimators.; Complete recipe for leak-proof end-to-end ColumnTransformer pipelines handling numerical imputation, scaling, and categorical encoding.; Recipe for saving, atomic loading, and metadata tracking of trained pipelines using joblib with SHA-256 integrity checks..
Recipe: Tune Hyperparameters (`GridSearchCV` & `RandomizedSearchCV`)
Systematic hyperparameter tuning recipe using GridSearchCV and RandomizedSearchCV with cross-validation on Random Forest estimators.
Recipe: Build a Leak-Proof Pipeline (`ColumnTransformer`)
Complete recipe for leak-proof end-to-end ColumnTransformer pipelines handling numerical imputation, scaling, and categorical encoding.
Recipe: Save, Load, & Version Model Artifacts (`joblib`)
Recipe for saving, atomic loading, and metadata tracking of trained pipelines using joblib with SHA-256 integrity checks.
Recipe: Production Classification & Threshold Calibration
Production classification recipe covering logistic regression, confusion matrices, ROC-AUC scoring, and custom decision threshold calibration.
Recipe: Mitigate Severe Class Imbalance (Weights vs. SMOTE)
Mitigation recipe for severe class imbalance comparing algorithm-native class_weight="balanced" against SMOTE oversampling.
Recipe: Feature Selection & Dimensionality Pruning
Feature selection recipe applying VarianceThreshold, SelectKBest with mutual information, and L1-penalty Lasso coefficient pruning.
Module 3: Generative AI, Fine-Tuning & Agentic Systems
7 Lessons • ~8.5 Study Hours (0.85 CEUs)Welcome to Module 3: Generative AI, Fine-Tuning & Agentic Systems. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Complete FastAPI microservice recipe for serving serialized scikit-learn models with strict Pydantic v2 schema validation.; Production QLoRA recipe fine-tuning an open-weights foundation model (Llama 3 8B) in 4-bit with Hugging Face PEFT and TRL.; Workflow recipe for fine-tuning proprietary closed models via OpenAI/Gemini APIs using JSONL validation and asynchronous polling..
Recipe: Serve Classical Models as Microservices (FastAPI)
Complete FastAPI microservice recipe for serving serialized scikit-learn models with strict Pydantic v2 schema validation.
Recipe: Fine-Tune an Open-Weights LLM (QLoRA + PEFT)
Production QLoRA recipe fine-tuning an open-weights foundation model (Llama 3 8B) in 4-bit with Hugging Face PEFT and TRL.
Recipe: Fine-Tune via a Closed API (OpenAI / Gemini)
Workflow recipe for fine-tuning proprietary closed models via OpenAI/Gemini APIs using JSONL validation and asynchronous polling.
Recipe: Implement Production RAG with LangChain & ChromaDB
End-to-end RAG recipe using LangChain, recursive character chunking, HuggingFace embeddings, and ChromaDB vector store.
Recipe: Build an Asynchronous LLM Streaming API (SSE)
High-throughput asynchronous FastAPI microservice recipe streaming LLM responses token-by-token using Server-Sent Events (SSE).
Recipe: Register & Execute Tool / Function Calling
Recipe for declaring tool schemas, invoking external APIs via LLM function calling, and returning deterministic structured tool responses.
Recipe: Build a Hybrid Classical + LLM Pipeline
Dual-stage hybrid pipeline recipe combining sub-2ms classical XGBoost triage routing with generative LLM clinical note synthesis.
Module 4: Production Monitoring & Deployment Readiness
3 Lessons • ~4.1 Study Hours (0.41 CEUs)Welcome to Module 4: Production Monitoring & Deployment Readiness. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Automated production monitoring recipe detecting numerical feature drift with the Kolmogorov-Smirnov test and Population Stability Index (PSI).; Twelve-point deployment readiness audit checklist covering validation, serialization, latency, drift, and fallback safety gates.; Master reference cheat sheets for GPU VRAM calculations, quantization bitwidths, hyperparameter matrices, and minimal environments..
Recipe: Detect & Monitor Production Drift (K-S Test & PSI)
Automated production monitoring recipe detecting numerical feature drift with the Kolmogorov-Smirnov test and Population Stability Index (PSI).
Production Deployment & Readiness Checklist
Twelve-point deployment readiness audit checklist covering validation, serialization, latency, drift, and fallback safety gates.
Master Cheat Sheets & Environment Reference
Master reference cheat sheets for GPU VRAM calculations, quantization bitwidths, hyperparameter matrices, and minimal environments.
Data Science & AI Progression
Continue advancing through the sequential curriculum stages of this academy track: