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

Module 1: Core Python for Machine Learning Pipelines

• Programme Launch and Workspace Configuration
Establishing learning objectives and configuring a reproducible Python ML environment

• Python Language Essentials (Accelerated Review)
Refreshing syntax, control structures, functions, and patterns frequently encountered in ML codebases

• Data Structures for ML Contexts
Utilizing lists, dictionaries, sets, and tuples for managing features, labels, and metadata

• Comprehensions and Functional Utilities
Implementing data transformations via list comprehensions and higher-order functions

• Object-Oriented Python for ML Developers
Applying classes, methods, and composition to make practical design choices

• dataclasses and Lightweight Modeling
Employing typed containers for configuration parameters, sample data, and result objects

• Decorators and Context Managers
Incorporating timing, caching, logging, and resource-safe execution patterns

• File I/O and Path Management
Ensuring robust dataset handling and managing serialization formats

• Exception Handling and Defensive Coding
Crafting ML scripts that handle failures safely and with transparency

• Modules, Packages, and Project Architecture
Structuring reusable ML codebases effectively

• Type Hinting and Code Quality
Implementing type hints, comprehensive documentation, and lint-compliant structures

Module 2: Numerical Python, SciPy, and Data Manipulation

• NumPy Foundations for Vectorized Computation
Optimizing array operations and writing performance-conscious code

• Indexing, Slicing, Broadcasting, and Shapes
Executing safe tensor manipulations and logical shape reasoning

• Linear Algebra Fundamentals with NumPy and SciPy
Performing stable matrix operations and decompositions relevant to ML

• SciPy Deep Dive
Exploring statistics, optimization, curve fitting, and sparse matrix handling

• Pandas for Tabular ML Data
Cleaning, merging, aggregating, and preparing datasets

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

• Visualization Essentials
Creating diagnostic plots for data exploration and analyzing model behavior

Module 3: Programming Patterns for ML Application Development

• Transitioning from Notebook to Maintainable Project
Refactoring exploratory code into well-structured packages

• Configuration Management
Implementing externalized parameters and startup validation mechanisms

• Logging, Warnings, and Observability
Establishing structured logging for debuggable ML systems

• Reusable Components via OOP and Composition
Designing extensible transformers and prediction modules

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

• Data Validation and Schema Checks
Preventing silent data integrity issues

• Performance Optimization and Profiling
Identifying bottlenecks and applying targeted 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 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
Utilizing regularized GLMs, tree ensembles, and leakage-free preprocessing

• Calibration and Uncertainty Quantification
Applying Platt scaling, isotonic regression, bootstrapping, and conformal prediction

• Classical NLP Methods
Analyzing tokenization trade-offs, TF-IDF, linear models, and Naive Bayes

• Topic Modeling
Understanding LDA fundamentals and practical limitations

• Classical Computer Vision
Employing HOG, PCA, and feature-based pipelines

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

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

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

• Mastering the Training Loop
Implementing clean PyTorch loops with AMP, gradient clipping, and reproducibility

• Optimization and Regularization
Managing initialization, normalization, optimizers, and schedulers

• Mixed Precision and Scaling Strategies
Utilizing gradient accumulation and checkpointing techniques

• Tabular Neural Networks
Implementing categorical embeddings, feature crosses, and ablation studies

• Text Neural Networks
Working with embeddings, CNNs, BiLSTM or GRU, and sequence processing

• Vision Neural Networks
Applying CNN fundamentals and ResNet-style architectures

• Practical Labs
Developing a reusable training framework
Comparing tabular NNs against boosting methods
Experimenting with CNNs using augmentation and scheduling

Module 6: Advanced Neural Architectures

• Transfer Learning Strategies
Applying freeze/unfreeze patterns and discriminative learning rates

• Transformer Architectures for Text
Exploring self-attention internals and fine-tuning methodologies

• Vision Backbones and Dense Prediction
Utilizing 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
Weighing the trade-offs of LoRA, distillation, and quantization

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

Module 7: Generative AI Systems

• Prompting Fundamentals
Employing structured prompting and controlled generation techniques

• LLM Foundations
Understanding tokenization, instruction tuning, and mitigating hallucinations

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

• Fine-Tuning Strategies
Applying LoRA and QLoRA with strict data quality controls

• Diffusion Models
Gaining intuition on latent diffusion and applying practical adaptations

• Synthetic Tabular Data
Using CTGAN while addressing privacy considerations

• Practical Labs
Building a production-style RAG mini-application
Implementing structured output validation with schema enforcement
Optional diffusion model 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
Applying episodic, semantic, and scratchpad approaches

• Tool Integration and Safety
Defining tool contracts, sandboxing, and defending against prompt injection

• Evaluation Frameworks
Creating replayable traces, task suites, and regression tests

• MCP and Protocol-Based Interoperability
Designing MCP servers with secure tool exposure

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