Generative AI Model Development and Fine Tuning
About This Course
In the era of advanced artificial intelligence, the ability to develop and fine-tune Generative AI (GenAI) models is critical for building high-performance, domain-specific solutions. This course, Generative AI Model Development and Fine Tuning, equips learners with the practical skills and technical knowledge to design, optimize, and evaluate modern AI models using cloud-based tools and frameworks.
Learners will begin by exploring techniques for data ingestion and transformation, including the use of synthetic data to enhance model performance and address data limitations. The course then focuses on building efficient data pipelines and feature engineering workflows, applying optimization strategies to improve model training and scalability.
Participants will gain hands-on experience in fine-tuning pre-trained multi-modal models, leveraging advanced training approaches, loss functions, and parameter optimization techniques to adapt models for specific use cases. Emphasis is placed on improving model accuracy, efficiency, and robustness in real-world deployment scenarios.
In addition, the course addresses critical considerations in modern AI development, including bias detection, explainability, and alignment with performance benchmarks. Learners will evaluate AI solutions to ensure they are reliable, ethical, and aligned with organizational and regulatory expectations.
By the end of the course, learners will be able to design end-to-end GenAI workflows—from data preparation to model fine-tuning and evaluation—enabling them to develop scalable, responsible, and high-performing AI solutions for a wide range of applications.
This course is suitable for data professionals, AI practitioners, and developers seeking to deepen their expertise in Generative AI model development, optimization, and fine-tuning techniques.
What You'll Learn
LO1: Apply transformation methods to ingest and prepare synthetic data using cloud-based model tools.
LO2: Develop optimized data pipelines for feature engineering using various optimization strategies.
LO3: Fine-tune pre-trained multi-modal models using advanced loss metrics and training strategies.
LO4: Analyse ML solutions for bias, explainability, and alignment with performance benchmarks.
Course Outline:
LU1: Data Engineering
T1: Create data repositories for machine learning (K2)
T2: Identify and implement data ingestion solutions (K5, A2)
T3: Identify and implement data transformation techniques (A3)
LU2: Exploratory Data Analysis
T1: Clean, sanitize, and prepare data for modelling (K6, A5)
T2: Perform feature engineering to enhance model performance (K8)
T3: Analyze and visualize data for machine learning insights (K7)
LU3: Modelling
T1: Frame business problems as machine learning problems (K4)
T2: Select appropriate models for different machine learning tasks (A1)
T3: Train and validate machine learning models (K3)
T4: Perform hyperparameter tuning and optimization (A4)
T5: Evaluate model performance using appropriate metrics (K1)
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target Year Group : 21-65 years old