AI Engineering Mastery: From Python Engineering to Production-Ready AI Systems Training Course

Module 1: Core Python for ML Workflows

• Course kickoff and environment setup
Align objectives and establish a reproducible Python ML workspace

• Python language essentials (fast-track)
Review syntax, control flow, functions, and patterns commonly used in ML codebases

• Data structures for ML
Utilise lists, dictionaries, sets, and tuples for features, labels, and metadata

• Comprehensions and functional tools
Express transformations using comprehensions and higher-order functions

• Object-oriented Python for ML developers
Master classes, methods, composition, and practical design decisions

• Dataclasses and lightweight modelling
Employ typed containers for configuration, examples, and results

• Decorators and context managers
Implement timing, caching, logging, and resource-safe execution patterns

• Working with files and paths
Handle robust datasets and serialization formats

• Exceptions and defensive programming
Write ML scripts that fail safely and transparently

• Modules, packages, and project structure
Organise reusable ML codebases effectively

• Typing and code quality
Apply type hints, documentation, and lint-friendly structures

Module 2: Numerical Python, SciPy and Data Handling

• NumPy foundations for vectorised computing
Perform efficient array operations and adopt performance-aware coding practices

• Indexing, slicing, broadcasting, and shapes
Manipulate tensors safely and reason about shapes effectively

• Linear algebra essentials with NumPy and SciPy
Execute stable matrix operations and decompositions relevant to ML

• SciPy deep dive
Explore statistics, optimisation, curve fitting, and sparse matrices

• Pandas for tabular ML data
Clean, join, aggregate, and prepare datasets efficiently

• scikit-learn deep dive
Master the estimator interface, pipelines, and reproducible workflows

• Visualisation essentials
Create diagnostic plots for data exploration and model behaviour analysis

Module 3: Programming Patterns for Building ML Applications

• From notebook to maintainable project
Refactor exploratory code into structured packages

• Configuration management
Manage externalised parameters and perform startup validation

• Logging, warnings, and observability
Implement structured logging for debuggable ML systems

• Reusable components with OOP and composition
Design extensible transformers and predictors

• Practical design patterns
Apply Pipeline, Factory or Registry, Strategy, and Adapter patterns

• Data validation and schema checks
Prevent silent data issues through rigorous validation

• Performance and profiling
Identify bottlenecks and apply optimisation techniques

• Model I O and inference interfaces
Ensure safe persistence and clean prediction interfaces

• End-to-end mini build
Construct a production-style ML pipeline complete with configuration and logging

Module 4: Statistical Learning for Tabular, Text and Image

• Evaluation foundations
Manage train and validation splits, honest cross-validation, and business-aligned metrics

• Advanced tabular ML
Utilise regularised GLMs, tree ensembles, and leakage-free preprocessing

• Calibration and uncertainty
Apply Platt scaling, isotonic regression, bootstrap, and conformal prediction

• Classical NLP methods
Navigate tokenisation trade-offs, TF-IDF, linear models, and Naive Bayes

• Topic modelling
Understand LDA fundamentals and their practical limitations

• Classical computer vision
Explore HOG, PCA, and feature-based pipelines

• Error analysis
Detect bias, label noise, and spurious correlations

• Hands-on labs
Leakage-proof tabular pipeline
Text baseline comparison and interpretation
Classical vision baseline with structured failure analysis

Module 5: Neural Networks for Tabular, Text and Image

• Training loop mastery
Implement clean PyTorch loops with AMP, clipping, and reproducibility measures

• Optimisation and regularisation
Handle initialisation, normalisation, optimisers, and schedulers effectively

• Mixed precision and scaling
Utilise gradient accumulation and checkpointing strategies

• Tabular neural networks
Use categorical embeddings, feature crosses, and ablation studies

• Text neural networks
Employ embeddings, CNNs, BiLSTM or GRU, and sequence handling techniques

• Vision neural networks
Master CNN fundamentals and ResNet-style architectures

• Hands-on labs
Reusable training framework
Tabular NN vs boosting comparison
CNN with augmentation and scheduling experiments

Module 6: Advanced Neural Architectures

• Transfer learning strategies
Apply freeze and unfreeze patterns, and discriminative learning rates

• Transformer architectures for text
Understand self-attention internals and fine-tuning approaches

• Vision backbones and dense prediction
Explore ResNet, EfficientNet, Vision Transformers, and U-Net concepts

• Advanced tabular architectures
Utilise TabTransformer, FT-Transformer, and Deep and Cross networks

• Time series considerations
Manage temporal splits and detect covariate shift

• PEFT and efficiency techniques
Navigate LoRA, distillation, and quantisation trade-offs

• Hands-on labs
Fine-tuning pretrained text transformer
Fine-tuning pretrained vision model
Tabular transformer vs GBDT comparison

Module 7: Generative AI Systems

• Prompting fundamentals
Implement structured prompting and controlled generation

• LLM foundations
Understand tokenisation, instruction tuning, and hallucination mitigation

• Retrieval-Augmented Generation
Master chunking, embeddings, hybrid search, and evaluation metrics

• Fine-tuning strategies
Apply LoRA and QLoRA with rigorous data quality controls

• Diffusion models
Grasp latent diffusion intuition and practical adaptation techniques

• Synthetic tabular data
Utilise CTGAN while considering privacy implications

• Hands-on labs
Production-style RAG mini-application
Structured output validation with schema enforcement
Optional diffusion experimentation

Module 8: AI Agents and MCP

• Agent loop design
Implement observe, plan, act, reflect, and persist cycles

• Agent architectures
Explore ReAct, plan-and-execute, and multi-agent coordination strategies

• Memory management
Utilise episodic, semantic, and scratchpad approaches

• Tool integration and safety
Establish tool contracts, sandboxing, and prompt injection defences

• Evaluation frameworks
Create replayable traces, task suites, and regression testing protocols

• MCP and protocol-based interoperability
Design MCP servers with secure tool exposure

• Hands-on labs
Build an agent from scratch
Expose tools via MCP-style server
Create evaluation harness with safety constraints