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

Module 1: Essential Python for Machine Learning Workflows

• Programme introduction and environment setup
Aligning objectives and establishing a reproducible Python ML workspace

• Python language essentials (accelerated)
Reviewing syntax, control flow, functions, and patterns prevalent in ML codebases

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

• Comprehensions and functional tools
Implementing transformations via comprehensions and higher-order functions

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

• dataclasses and lightweight modelling
Using typed containers for configuration, examples, and results

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

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

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

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

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

Module 2: NumPy, SciPy, and Data Handling

• NumPy foundations for vectorised computing
Executing efficient array operations and performance-aware coding

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

• Linear algebra essentials with NumPy and SciPy
Performing stable matrix operations and decompositions used in ML

• Deep dive into SciPy
Covering statistics, optimisation, curve fitting, and sparse matrices

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

• Deep dive into scikit-learn
Mastering the estimator interface, pipelines, and reproducible workflows

• Visualisation essentials
Creating diagnostic plots for data exploration and model behavior analysis

Module 3: Programming Patterns for Building ML Applications

• Transitioning from notebook to maintainable project
Refactoring exploratory code into structured packages

• Configuration management
Implementing externalized parameters and startup validation

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

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

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

• Data validation and schema checks
Preventing silent data issues

• Performance and profiling
Identifying bottlenecks and applying optimization techniques

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

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

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

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

• Advanced tabular ML
Utilizing regularized GLMs, tree ensembles, and leakage-free preprocessing

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

• Classical NLP methods
Understanding tokenization trade-offs, TF-IDF, linear models, and Naive Bayes

• Topic modelling
Covering LDA fundamentals and practical limitations

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

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

• Hands-on labs
Building a leakage-proof tabular pipeline
Comparing and interpreting text baselines
Analyzing classical vision baselines with structured failure analysis

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

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

• Optimisation and regularisation
Mastering initialization, normalization, optimizers, and schedulers

• Mixed precision and scaling
Implementing gradient accumulation and checkpointing strategies

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

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

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

• Hands-on labs
Building a reusable training framework
Comparing Tabular NN vs boosting
Conducting CNN experiments with augmentation and scheduling

Module 6: Advanced Neural Architectures

• Transfer learning strategies
Employing 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
Managing temporal splits and detecting covariate shift

• PEFT and efficiency techniques
Navigating trade-offs in LoRA, distillation, and quantization

• Hands-on labs
Fine-tuning a pretrained text transformer
Fine-tuning a pretrained vision model
Comparing tabular transformers vs GBDT

Module 7: Generative AI Systems

• Prompting fundamentals
Utilizing structured prompting and controlled generation

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

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

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

• Diffusion models
Gaining intuition for latent diffusion and practical adaptation

• Synthetic tabular data
Using CTGAN and addressing privacy considerations

• Hands-on labs
Building a production-style RAG mini-application
Validating structured output 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
Utilizing episodic, semantic, and scratchpad approaches

• Tool integration and safety
Establishing tool contracts, sandboxing, and prompt injection defenses

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

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

• Hands-on labs
Building an agent from scratch
Exposing tools via an MCP-style server
Creating an evaluation harness with safety constraints