About This Course
Artificial intelligence is no longer an emerging frontier — it is an active, production-critical layer of modern technical infrastructure. From intelligent automation and predictive analytics to natural language processing and computer vision, AI capabilities are being embedded into enterprise systems, cloud platforms, and operational pipelines at an accelerating pace. Technical staff who lack a structured understanding of how these systems are built, trained, evaluated, and governed are increasingly at risk of being left behind — or worse, of deploying solutions that fail silently in high-stakes environments.
This ten-day intensive programme from TrainingComplex is designed specifically for working technical professionals: software engineers, data engineers, system architects, DevOps practitioners, and IT specialists who need more than a conceptual overview. Participants will work through the complete AI development lifecycle — from data acquisition and feature engineering through model selection, training, evaluation, deployment, and monitoring — using industry-standard tools and frameworks including Python, scikit-learn, TensorFlow, and MLflow.
The curriculum goes beyond model building to address the operational realities that technical teams face: integrating AI services into existing architectures, managing model drift and retraining pipelines, applying explainability techniques, and implementing security and compliance controls for AI systems. Participants also engage with responsible AI principles, bias detection, and governance frameworks that are increasingly required by regulators and enterprise clients alike.
By the end of the programme, each participant will have built and deployed a functional AI model, documented it against professional standards, and developed a concrete roadmap for applying these capabilities within their own organisation. TrainingComplex's cohort-based format ensures that learning is collaborative, peer-driven, and directly anchored to the technical environments participants work in every day.
Who Should Attend
- Software engineers and developers seeking to build AI-powered applications and services
- Data engineers responsible for building and maintaining data pipelines that feed machine learning systems
- System architects designing enterprise platforms that incorporate AI components
- DevOps and MLOps engineers tasked with deploying and monitoring AI models in production
- IT infrastructure specialists supporting AI workloads on cloud or on-premise environments
- Technical team leads who need to evaluate, guide, and quality-assure AI development work
- BI and analytics professionals looking to extend their skill set into predictive and prescriptive modelling
- Cybersecurity professionals addressing the unique risks introduced by AI systems
Learning Objectives
- Explain core AI and machine learning concepts, taxonomies, and use-case categories relevant to enterprise technical environments
- Set up and navigate professional AI development environments using Python, Jupyter, and version-controlled repositories
- Apply data acquisition, cleaning, and feature engineering techniques to prepare datasets for model training
- Select and implement appropriate supervised, unsupervised, and semi-supervised learning algorithms for given technical problems
- Design, train, evaluate, and tune machine learning models using scikit-learn and TensorFlow
- Build and deploy deep learning models for structured data, image recognition, and natural language tasks
- Construct end-to-end MLOps pipelines covering experiment tracking, model versioning, CI/CD integration, and production deployment
- Monitor deployed models for performance degradation, concept drift, and data quality issues
- Apply explainability and interpretability techniques including SHAP values, LIME, and model cards
- Integrate AI APIs and pre-trained models from cloud providers into existing technical architectures
- Identify and mitigate bias, fairness issues, and security vulnerabilities specific to AI systems
- Implement responsible AI governance frameworks aligned with organisational and regulatory requirements
- Develop and present a documented AI solution addressing a real technical challenge from participants' own organisations
Course Modules
Module 1: Foundations of Artificial Intelligence for Technical Professionals
- AI taxonomy: narrow AI, machine learning, deep learning, and generative AI
- Key AI application domains: computer vision, NLP, predictive analytics, and robotics
- The AI development lifecycle from problem framing to production retirement
- Differentiating AI from rule-based systems and traditional software engineering
- Current AI landscape: open-source ecosystems, cloud platforms, and commercial APIs
- Ethical and business considerations that shape technical AI decisions
Module 2: Setting Up the AI Development Environment
- Python for AI: environment management with conda and virtual environments
- Essential libraries: NumPy, Pandas, Matplotlib, scikit-learn, and TensorFlow
- Jupyter notebooks and VS Code for structured AI experimentation
- Version control for AI projects: Git branching strategies and repository structure
- Data storage and access patterns: local, cloud object storage, and data lakes
- Reproducibility principles: seeds, environment pinning, and experiment logging
Module 3: Data Acquisition, Exploration, and Quality Assessment
- Data sourcing strategies: open datasets, APIs, web scraping, and enterprise data stores
- Exploratory data analysis techniques using Pandas and Matplotlib
- Data quality dimensions: completeness, consistency, timeliness, and accuracy
- Handling missing values, outliers, and duplicate records systematically
- Data profiling tools and automated quality reports
- Documenting data lineage and provenance for auditability
Module 4: Feature Engineering and Data Preprocessing
- Encoding categorical variables: one-hot, ordinal, and target encoding
- Scaling and normalisation techniques and when to apply each
- Feature selection methods: filter, wrapper, and embedded approaches
- Dimensionality reduction with PCA and t-SNE
- Handling class imbalance with SMOTE and resampling strategies
- Building reusable preprocessing pipelines with scikit-learn Pipeline and ColumnTransformer
Module 5: Supervised Learning: Classification and Regression
- Linear and logistic regression: assumptions, implementation, and diagnostics
- Decision trees, random forests, and gradient boosting with XGBoost
- Support vector machines and k-nearest neighbours for classification tasks
- Model evaluation metrics: accuracy, precision, recall, F1, AUC-ROC, and RMSE
- Cross-validation strategies and avoiding data leakage
- Hyperparameter tuning with GridSearchCV and Optuna
Module 6: Unsupervised Learning and Semi-Supervised Techniques
- K-means and hierarchical clustering: algorithms, distance metrics, and cluster validation
- DBSCAN for density-based anomaly and outlier detection
- Dimensionality reduction for visualisation and compression
- Association rule mining with the Apriori algorithm
- Semi-supervised learning: label propagation and self-training frameworks
- Practical use cases: customer segmentation, log anomaly detection, and recommendation seeding
Module 7: Deep Learning and Neural Network Architectures
- Perceptrons, activation functions, and the mathematics of backpropagation
- Building and training feedforward neural networks with TensorFlow and Keras
- Convolutional neural networks for image classification and object detection
- Recurrent neural networks and LSTMs for sequential and time-series data
- Transfer learning: fine-tuning pre-trained models for domain-specific tasks
- Regularisation techniques: dropout, batch normalisation, and early stopping
Module 8: Natural Language Processing and Generative AI Integration
- Text preprocessing: tokenisation, stemming, lemmatisation, and vectorisation
- Classical NLP models: TF-IDF, Naive Bayes, and sentiment analysis pipelines
- Word embeddings: Word2Vec, GloVe, and contextual embeddings with BERT
- Integrating large language model APIs: OpenAI, Google Gemini, and Azure OpenAI
- Prompt engineering principles for technical and enterprise use cases
- Building retrieval-augmented generation (RAG) pipelines for domain-specific Q&A
Module 9: MLOps: Experiment Tracking, Model Versioning, and CI/CD
- MLOps maturity levels and the gap between notebook experimentation and production ML
- Experiment tracking and model registry with MLflow
- Containerising ML models with Docker for consistent deployment environments
- CI/CD pipelines for ML: automated testing, linting, and deployment with GitHub Actions
- Model serving patterns: REST APIs with FastAPI, batch inference, and streaming inference
- Infrastructure-as-code principles applied to AI workload provisioning
Module 10: Model Monitoring, Drift Detection, and Retraining Pipelines
- Types of model degradation: data drift, concept drift, and covariate shift
- Setting up monitoring dashboards with Evidently AI and Prometheus
- Statistical tests for drift detection: KS test, PSI, and Jensen-Shannon divergence
- Defining retraining triggers: threshold-based, scheduled, and event-driven strategies
- Automated retraining pipelines and canary deployment for model updates
- Incident response workflows for AI model failures in production
Module 11: AI Explainability, Fairness, and Bias Mitigation
- Why explainability matters: regulatory drivers, user trust, and debugging value
- Model-agnostic explanation methods: SHAP values and LIME in practice
- Global vs. local interpretability and when each is appropriate
- Measuring algorithmic fairness: demographic parity, equalised odds, and calibration
- Bias sources in data, labelling, and model design — and mitigation strategies
- Producing model cards and datasheets as professional documentation artefacts
Module 12: AI Security, Cloud Integration, and Architecture Patterns
- AI-specific threat vectors: adversarial attacks, model inversion, and data poisoning
- Securing model endpoints: authentication, rate limiting, and input validation
- Cloud AI services: AWS SageMaker, Google Vertex AI, and Azure Machine Learning
- Architecture patterns for embedding AI into enterprise systems: microservices, event-driven, and edge AI
- Cost management for AI workloads: compute optimisation, spot instances, and caching strategies
- Governance controls for AI in regulated industries: audit trails, access controls, and compliance logging
Module 13: Responsible AI Governance and Capstone Project Delivery
- Responsible AI frameworks: EU AI Act, NIST AI RMF, and ISO/IEC 42001 overview
- Organisational AI governance structures: roles, review boards, and policy development
- Risk classification of AI systems and proportionate control requirements
- Capstone project presentations: defending AI solution design, methodology, and governance approach
- Peer and facilitator feedback structured around professional technical review criteria
- Building a personal AI skills roadmap and next steps for organisational implementation
Requirements & Prerequisites
Participants should have solid working knowledge of at least one programming language, with Python experience strongly preferred. A foundational understanding of statistics, linear algebra, and data structures is recommended to engage fully with the modelling content. Prior exposure to cloud platforms or databases is helpful but not required, as these concepts are introduced progressively throughout the programme.
Training Methodology
- Guided hands-on lab sessions using pre-configured Python environments, real datasets, and version-controlled notebooks for every major concept
- End-to-end project sprints where participants build, evaluate, and deploy a working AI model incrementally across the ten days
- Debugging and troubleshooting workshops that present broken or underperforming models for participants to diagnose and fix
- Peer code-review exercises that mirror professional pull-request and quality-assurance workflows used in engineering teams
- Case study analyses of real AI deployment failures and successes, drawing lessons on architecture, governance, and operational risk
- Live demonstrations of MLOps tooling including MLflow, Docker-based deployment, and cloud AI service integration
- Bias audit exercises where participants apply fairness metrics and SHAP-based explainability to their own trained models
- Presentation and critique sessions where each participant or team defends their AI solution design to the group, simulating a technical review board
Organisational Benefits
- Builds a resident AI engineering capability within the organisation, reducing dependence on expensive external consultants
- Accelerates time-to-deployment for AI initiatives by equipping technical staff with end-to-end pipeline skills
- Reduces the risk of costly production failures through rigorous model evaluation, monitoring, and governance practices
- Strengthens data infrastructure quality as teams apply professional feature engineering and pipeline standards
- Enhances the organisation's ability to leverage cloud AI services and APIs cost-effectively and securely
- Supports compliance with emerging AI regulations and enterprise data governance requirements
- Fosters cross-functional collaboration between data science, engineering, and operations teams through shared MLOps language and tooling
- Positions the organisation competitively by embedding AI capabilities directly into product and service delivery workflows
Where Is AI Training for Technical Staff Training Offered?
This course runs in-person across: Kampala, Johannesburg, Nakuru, Nairobi, Dubai, Dar es Salaam, Abuja, Addis Ababa, Pretoria, Naivasha, Arusha, Kisumu, Accra, Zanzibar, Mombasa, Cape Town, Kigali, Lagos, Riyadh, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Kampala, Uganda |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 3,800 |
| Johannesburg, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 7,000 |
| Nakuru, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Nairobi, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Dubai, United Arab Emirates |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 8,200 |
| Dar es Salaam, Tanzania |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 4,600 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 5,600 |
| Addis Ababa, Ethiopia |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,800 |
| Pretoria, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 6,600 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
KES 230,000 |
| Arusha, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,200 |
| Live Virtual |
Online |
19 Oct 2026 – 28 Oct 2026 |
KES 160,000 |
| Kisumu, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 6 Nov 2026 |
USD 7,600 |
+232 more dates — view live schedule →
Frequently Asked Questions
Do I need prior machine learning experience to attend this course?
No prior machine learning experience is required, but participants should be comfortable programming in Python and have a working understanding of basic statistics and data structures. The course builds AI knowledge progressively from fundamentals, so engineers with strong software or data backgrounds will find the transition smooth.
What tools and software will we use during the programme?
The course uses Python as the primary language, with hands-on work in scikit-learn, TensorFlow, Keras, MLflow, Docker, and FastAPI. Participants will also integrate cloud AI APIs from providers such as OpenAI and Azure. All environments are pre-configured — participants need only bring a laptop capable of running a modern browser.
Will I receive a certificate upon completing the course?
Yes. Participants who attend the full programme and complete the capstone project will receive a TrainingComplex Certificate of Completion in AI for Technical Staff. The certificate includes a summary of competencies covered and is suitable for inclusion in professional portfolios and CPD records.
Is this course suitable for someone who manages AI projects rather than builds models?
The programme is designed for hands-on technical practitioners rather than project managers or business stakeholders. Technical team leads who write or review code will benefit fully; those in purely managerial roles would be better served by TrainingComplex's AI for Leaders and Decision-Makers programme.
How is the capstone project structured?
Each participant develops an AI solution addressing a real technical challenge from their own organisation or a provided case scenario. The solution is built incrementally across the ten days, culminating in a structured presentation and peer review on the final day. Facilitators provide feedback at each stage to ensure the project meets professional engineering standards.
