Applied AI Systems & Machine Learning Engineering
Build end-to-end ML pipelines, MLOps serving, LangChain RAG, and QLoRA fine-tuning
The core systems engineering curriculum for AI practitioners. Journey from linear and logistic regression models through leakage-free scikit-learn transformers, model evaluation under class imbalance, containerized model serving with FastAPI and Docker, to cutting-edge Generative AI: LangChain/LlamaIndex vector databases, retrieval-augmented generation (RAG), and PEFT/QLoRA parameter-efficient fine-tuning on consumer GPUs.
Course Prerequisites
- Exploratory Data Analysis & Statistical Computing with Python or equivalent Python data analysis expertise.
Part of Academy Track:
What You Will Master
Curriculum Modules (5 Modules)
Explore the structured module breakdown, lesson outcomes, and practical lab exercises.
Module 1: Part I: Foundations of Data & Statistical Modeling
3 Lessons • ~3.7 Study Hours (0.37 CEUs)Welcome to Module 1: Part I: Foundations of Data & Statistical Modeling. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Understand the unified nesting framework connecting statistical modeling, supervised learning, and foundation models.; Rigorous statistical modeling, Ordinary Least Squares (OLS), p-values, hypothesis testing, and coefficient interpretation using statsmodels.; Transitioning from exploratory data analysis and descriptive statistics to predictive machine learning problem formulation..
Big Picture: How the Concepts Relate
Understand the unified nesting framework connecting statistical modeling, supervised learning, and foundation models.
Statistical Modeling & Regression Analysis (`statsmodels`)
Rigorous statistical modeling, Ordinary Least Squares (OLS), p-values, hypothesis testing, and coefficient interpretation using statsmodels.
From Exploratory Data Analysis (EDA) to Machine Learning
Transitioning from exploratory data analysis and descriptive statistics to predictive machine learning problem formulation.
Module 2: Part II: Classical Machine Learning & Pipeline Engineering
8 Lessons • ~11.7 Study Hours (1.17 CEUs)Welcome to Module 2: Part II: 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: Supervised regression mechanics, comparing Linear Regression, Ridge, Lasso, Decision Trees, and Random Forests with R², MAE, and RMSE evaluation.; Classification workflows, probability thresholds, confusion matrices, ROC-AUC, and the trade-off between Precision and Recall.; Strategic mitigation of extreme class imbalance using class weighting, focal loss, and resampling techniques (SMOTE)..
Supervised Regression: Linear Regression to Random Forest
Supervised regression mechanics, comparing Linear Regression, Ridge, Lasso, Decision Trees, and Random Forests with R², MAE, and RMSE evaluation.
Supervised Classification: Workflows & Evaluation Metrics
Classification workflows, probability thresholds, confusion matrices, ROC-AUC, and the trade-off between Precision and Recall.
Handling Severe Class Imbalance
Strategic mitigation of extreme class imbalance using class weighting, focal loss, and resampling techniques (SMOTE).
Feature Engineering & Feature Selection
Feature engineering best practices, variance thresholding, mutual information, and L1-regularization for feature selection.
Gradient Boosting Mastery: XGBoost Deep Dive
Gradient boosting mathematical deep dive, tree structure regularization, objective functions, and XGBoost production tuning.
Hyperparameter Optimization: Mechanical Breakdown of `GridSearchCV`
Mechanical breakdown of hyperparameter optimization, comparing GridSearchCV, RandomizedSearchCV, and Bayesian optimization with Stratified K-Fold validation.
Building Leak-Proof Production Pipelines
Engineering leak-proof scikit-learn pipelines with ColumnTransformer, robust imputers, and standard encoders to guarantee data hygiene.
Model Persistence & Artifact Versioning (`joblib`)
Serialization, versioning, and metadata tracking of trained pipelines using joblib and cryptographic checksums.
Module 3: Part III: Classical MLOps & Production Serving
4 Lessons • ~5.1 Study Hours (0.51 CEUs)Welcome to Module 3: Part III: Classical MLOps & Production Serving. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Comparing batch prediction, real-time REST inference, and streaming architectures for machine learning deployments.; Serving production machine learning models as high-throughput microservices using FastAPI and Pydantic v2 validation.; Differentiating data drift from concept drift, implementing automated Kolmogorov-Smirnov statistical tests, and trigger-based retraining policies..
Real-World Production Architecture: Batch vs. Real-Time
Comparing batch prediction, real-time REST inference, and streaming architectures for machine learning deployments.
Serving Classical Models as Microservices with FastAPI
Serving production machine learning models as high-throughput microservices using FastAPI and Pydantic v2 validation.
Model Monitoring: Detecting & Mitigating Data Drift vs. Concept Drift
Differentiating data drift from concept drift, implementing automated Kolmogorov-Smirnov statistical tests, and trigger-based retraining policies.
Modern MLOps: Automated Retraining, Experiment Tracking, & Containers
Modern MLOps foundations, automated retraining pipelines, experiment tracking, model registries, and Docker containerization.
Module 4: Part IV: Generative AI & Large Language Models (LLMs)
10 Lessons • ~12.4 Study Hours (1.24 CEUs)Welcome to Module 4: Part IV: Generative AI & Large Language Models (LLMs). In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: Pragmatic decision matrix for choosing between classical tabular models, specialized neural architectures, and Large Language Models.; Hardware sizing mathematics, parameter scales (1B to 405B), FP16/BF16 vs. 4-bit/8-bit quantization, and VRAM requirement formulas.; Designing asynchronous FastAPI endpoints for LLMs, token streaming via Server-Sent Events (SSE), and concurrency backpressure management..
Classical ML vs. Large Language Models: Pragmatic Decision Framework
Pragmatic decision matrix for choosing between classical tabular models, specialized neural architectures, and Large Language Models.
Model Scales, VRAM Sizing, & Quantization (1B to 405B)
Hardware sizing mathematics, parameter scales (1B to 405B), FP16/BF16 vs. 4-bit/8-bit quantization, and VRAM requirement formulas.
Serving Generative AI: FastAPI, Token Streaming (SSE), & Async Design
Designing asynchronous FastAPI endpoints for LLMs, token streaming via Server-Sent Events (SSE), and concurrency backpressure management.
LLM Adaptation Strategies: Prompting vs. RAG vs. Fine-Tuning
Strategic trade-off analysis between Prompt Engineering, In-Context Learning, Retrieval-Augmented Generation (RAG), and Fine-Tuning.
Efficient LLM Fine-Tuning: Deep Dive into LoRA & QLoRA
Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) mathematical mechanics, rank selection, alpha scaling, and target module tuning.
Fine-Tuning Workflows: Closed APIs vs. Open-Weights
Decision trade-offs and architectural comparison between closed-API fine-tuning and self-hosted open-weights fine-tuning.
Production Retrieval-Augmented Generation (RAG) with LangChain
Production-grade Retrieval-Augmented Generation (RAG) using LangChain, recursive character chunking, semantic vector embeddings, and vector stores.
Function Calling & Tool Calling: Building Agentic Workflows
Building deterministic agentic workflows using LLM function calling, Pydantic tool schemas, and structured JSON generation.
Hybrid Production Pipelines: Classical ML + Generative LLMs
Architecting dual-stage hybrid pipelines that combine deterministic classical ML safety/routing gates with generative LLM synthesis.
Case Study: AfriPulse AI (Clinical Triage & Telemedicine System)
End-to-end case study of AfriPulse AI: clinical triage microservice featuring vital-signs gating, LoRA fine-tuning, and streaming SOAP note generation.
Module 5: Part V: Career & Reference
2 Lessons • ~2.3 Study Hours (0.23 CEUs)Welcome to Module 5: Part V: Career & Reference. In this section of the curriculum, learners dive deep into foundational and advanced principles designed for production application. This module covers: System design framework, STAR-formatted behavioral walkthroughs, and architectural failure scenario questions for AI engineering roles.; Master glossary of key mathematical, statistical, machine learning, MLOps, and generative AI terms and definitions..
AI Software Engineer Interview Mastery
System design framework, STAR-formatted behavioral walkthroughs, and architectural failure scenario questions for AI engineering roles.
The AI Engineering Master Glossary
Master glossary of key mathematical, statistical, machine learning, MLOps, and generative AI terms and definitions.
Data Science & AI Progression
Continue advancing through the sequential curriculum stages of this academy track: