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

Module 1: Core Python for ML Workflows

• Course introduction and workspace configuration
Aligning learning objectives and establishing a reproducible Python ML environment

• Accelerated Python syntax review
Revisiting essential syntax, control flow, functions, and patterns frequently found in ML codebases

• Data structures for ML applications
Utilising lists, dictionaries, sets, and tuples to manage features, labels, and metadata

• Comprehensions and functional programming tools
Implementing data transformations through comprehensions and higher-order functions

• Object-oriented Python for ML developers
Exploring classes, methods, composition, and key design decisions in practice

• dataclasses and lightweight data modelling
Creating typed containers for configurations, data examples, and results

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

• File and path management
Ensuring robust dataset handling and efficient serialization formats

• Exception handling and defensive coding
Writing ML scripts that handle errors safely and transparently

• Modules, packages, and project organisation
Structuring reusable ML codebases effectively

• Type hinting and code quality
Incorporating type hints, documentation, and lint-friendly code structures

Module 2: Numerical Python, SciPy, and Data Processing

• NumPy basics for vectorised computation
Performing efficient array operations with an eye on performance

• Indexing, slicing, broadcasting, and shapes
Mastering safe tensor manipulation and shape reasoning

• Essential linear algebra with NumPy and SciPy
Applying stable matrix operations and decompositions relevant to ML

• In-depth SciPy exploration
Covering statistics, optimisation, curve fitting, and sparse matrices

• Pandas for tabular ML data
Cleaning, joining, aggregating, and preparing datasets for analysis

• Comprehensive scikit-learn guide
Understanding the estimator interface, pipelines, and reproducible workflows

• Visualisation basics
Creating diagnostic plots for data exploration and assessing model behaviour

Module 3: Programming Patterns for ML Applications

• Transitioning from notebooks to maintainable projects
Refactoring exploratory code into well-structured packages

• Configuration management
Implementing externalised parameters and startup validation

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

• Reusable components via OOP and composition
Designing extensible transformers and predictors

• Applied design patterns
Utilising Pipeline, Factory or Registry, Strategy, and Adapter patterns

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

• Performance optimisation and profiling
Identifying bottlenecks and applying targeted optimisation techniques

• Model I/O and inference interfaces
Ensuring safe persistence and clean prediction interfaces

• End-to-end mini project
Building a production-style ML pipeline with integrated configuration and logging

Module 4: Statistical Learning for Tabular, Text, and Image Data

• Evaluation fundamentals
Implementing train/validation splits, honest cross-validation, and business-aligned metrics

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

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

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

• Topic modelling
Understanding LDA fundamentals and their practical limitations

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

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

• Practical laboratories
Building a leakage-proof tabular pipeline
Comparing and interpreting text baselines
Analysing classical vision baselines with structured failure analysis

Module 5: Neural Networks for Tabular, Text, and Image Data

• Mastery of training loops
Implementing clean PyTorch loops with AMP, clipping, and reproducibility

• Optimisation and regularisation strategies
Managing initialisation, normalisation, optimisers, and schedulers

• Mixed precision and scaling
Utilising gradient accumulation and checkpointing strategies

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

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

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

• Practical laboratories
Developing a reusable training framework
Comparing tabular NNs against boosting
Experimenting with CNNs, augmentation, and scheduling

Module 6: Advanced Neural Architectures

• Transfer learning strategies
Applying freeze/unfreeze patterns and discriminative learning rates

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

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

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

• Time series considerations
Handling temporal splits and detecting covariate shift

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

• Practical laboratories
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing tabular transformers against GBDT

Module 7: Generative AI Systems

• Prompting fundamentals
Applying structured prompting and controlled generation

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

• Retrieval-Augmented Generation (RAG)
Implementing chunking, embeddings, hybrid search, and evaluation metrics

• Fine-tuning strategies
Using LoRA and QLoRA with strict data quality controls

• Diffusion models
Understanding latent diffusion intuition and practical adaptation

• Synthetic tabular data
Using CTGAN and addressing privacy considerations

• Practical laboratories
Developing a production-style RAG mini-application
Validating structured outputs with schema enforcement
Optional diffusion experimentation

Module 8: AI Agents and MCP

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

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

• Memory management
Utilising episodic, semantic, and scratchpad approaches

• Tool integration and safety
Defining tool contracts, sandboxing, and defending against prompt injection

• Evaluation frameworks
Implementing replayable traces, task suites, and regression testing

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

• Practical laboratories
Building an agent from scratch
Exposing tools via an MCP-style server
Creating an evaluation harness with safety constraints