NEVADA | PROFESSIONAL EXPERIENCE

Program Professional Framework

Artificial Intelligence (AI) Mastery for Technical Professionals

Program Overview

This extended professional program takes engineers, developers and technical specialists from solid machine learning foundations to designing, deploying and governing AI systems at production scale. It covers data engineering, classical machine learning, deep learning for vision, time series and language, large language models and retrieval-augmented generation, agentic systems, MLOps and model monitoring, and edge and industrial AI. A strategic layer addresses AI architecture decisions, governance under frameworks such as ISO/IEC 42001 and the NIST AI RMF, and an applied research project in which participants frame, test and report on an AI solution for a real problem in their organization.

Learning Outcomes

✓Build end-to-end machine learning pipelines in Python covering data preparation, training, validation and tuning.
✓Develop deep learning models for computer vision, time series or text and evaluate them with appropriate metrics.
✓Design LLM-based applications using retrieval-augmented generation, fine-tuning decisions and agent patterns.
✓Implement MLOps practices including versioning, CI/CD for models, monitoring and drift detection.
✓Prepare an AI architecture and governance design aligned with ISO/IEC 42001 and NIST AI RMF.
✓Conduct and report an applied AI investigation with a clear hypothesis, experiment design and evaluation.

Target Audience

Software, data and controls engineers
Data scientists seeking advanced and production skills
IT architects and technical leads in digital transformation
Engineers in energy, utilities and industrial operations applying AI

Detailed Program Modules

01Mathematical and Programming Foundations

Linear algebra, probability and optimization essentials
Python, NumPy, pandas and scikit-learn
Reproducible experiment environments

02Data Engineering for AI

Data pipelines, quality and labelling
Feature stores and data versioning
Working with industrial and historian data

03Classical Machine Learning

Regression, classification and ensemble methods
Model selection, tuning and validation
Interpretability methods such as SHAP

04Deep Learning

Neural network training and regularization
Convolutional networks for vision tasks
Sequence models and time series forecasting

05Large Language Models and RAG

Transformer architecture and embeddings
Retrieval-augmented generation design
Fine-tuning versus prompting decisions

06Agentic and Multimodal Systems

Tool use and agent orchestration
Multimodal models for text, image and documents
Evaluation of agent reliability

07MLOps and Production Deployment

Model packaging, serving and CI/CD
Monitoring, drift and retraining
Cost and performance optimization

08Edge and Industrial AI

Predictive maintenance and anomaly detection
Deploying models on edge devices
Integration with OT systems and cybersecurity

09AI Architecture and Governance

Reference architectures and build-or-buy choices
ISO/IEC 42001 and NIST AI RMF controls
Responsible AI, privacy and data residency

10Applied AI Research Project

Problem framing and hypothesis definition
Experiment design and benchmarking
Technical reporting and stakeholder presentation

Training Methodology

Interactive expert-led sessions grounded in workplace examples

Case studies and practical exercises drawn from the region

Group workshops with peer review of decisions and deliverables

Formative feedback against clear performance criteria

Applied Project & Assessment

Applied Project

Participants complete an applied AI project on a real technical problem: data pipeline, trained and evaluated model or LLM application, deployment and monitoring design, governance assessment and a technical report presented to a review panel.

Assessment

Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.

University Certificate & International Accreditation

Professional Certificate accredited by Universiti Teknologi Malaysia (UTM) in Artificial Intelligence (AI) Mastery for Technical Professionals

International accreditation: American Association of Innovation (AAI)