WSQ - Generative AI Model Development and Fine Tuning

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)

Final Assessment

• Written Assessment (SAQ)
• Practical Performance 

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions
• 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 Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

• Git Lab

Hardware: Window or Mac Laptops

• Machine Learning Engineer
• Data Scientist
• AWS ML Specialist
• AI Engineer
• Data Engineer
• Cloud Machine Learning Architect
• ML Operations Engineer
• AI/ML Consultant
• Applied Scientist
• Deep Learning Engineer
• Business Intelligence Developer
• Cloud Solutions Architect
• Data Analyst
• DevOps Engineer (ML-focused)
• Technical Product Manager (AI/ML)
• AI Research Engineer
• Software Engineer (ML Integration)
• Big Data Specialist
• IT Systems Engineer (AI Tools)
• Automation Engineer (AI/ML)

Amin Mahetar: Amin Mahetar is a seasoned Cloud Security Architect with extensive experience designing and implementing secure, scalable solutions across AWS, Azure, and GCP environments. With certifications including AWS Security Specialty, AWS Solution Architect Associate, and CISSP, he has successfully led major digital transformation projects at Cisco and GovTech Singapore. His expertise spans cloud security architecture, IAM and SSO implementation, data protection, and compliance with international frameworks such as ISO 27001, PDPA, and NIST. Amin’s multidisciplinary background allows him to bridge security governance with practical cloud operations, ensuring resilient and compliant infrastructures for enterprise clients.

In the AWS Certified Machine Learning Specialty Training course, Amin focuses on secure AI and ML system design within the AWS ecosystem. He guides learners on applying best practices in data governance, model protection, and risk management while deploying ML workloads using AWS services. His practical approach ensures participants gain both technical and compliance-oriented competencies essential for building robust machine learning pipelines in production environments.

Mohan Pothula: Mohan Pothula is an accomplished Enterprise Architect with over 20 years of experience leading data strategy, AI adoption, and cloud modernization initiatives for global financial institutions and enterprises. He has designed large-scale AWS-based architectures for DBS Bank, SPH, and Mediacorp, integrating data platforms with microservices and big data analytics frameworks. His expertise covers enterprise data architecture, real-time analytics, cloud migration, and the implementation of CI/CD pipelines for scalable AI-driven systems. Mohan’s deep understanding of both business and technology domains enables him to align organizational strategy with cloud-native and machine learning capabilities.

In this AWS Certified Machine Learning Specialty Training course, Mohan provides a strategic and hands-on perspective on developing and deploying ML solutions at scale. He emphasizes architecting AI pipelines using AWS services such as SageMaker, Glue, Redshift, and Lambda—ensuring they meet enterprise-level performance, scalability, and compliance requirements. His sessions help learners master the intersection of data engineering and AI deployment within secure AWS infrastructures.

Quah Chee Yong: Quah Chee Yong is a data science educator and AI specialist with deep expertise in machine learning, natural language processing, and predictive analytics. As a WSQ ACLP-certified adult educator, he has led national training programs under SAP, Temasek Polytechnic, and IMDA, specializing in data science upskilling for professionals and organizations. His industry experience includes developing recommender systems, NLP models, and chatbot applications using Python, TensorFlow, and Google Cloud. With a strong background in data analytics and applied AI, Chee Yong effectively bridges theoretical concepts with real-world applications.

In this AWS Certified Machine Learning Specialty Training course, Chee Yong trains participants to leverage AWS ML tools for data processing, model training, and intelligent automation. His approach combines practical project work with in-depth exploration of SageMaker, S3, and related AWS services. Learners benefit from his ability to translate complex algorithms into actionable insights, equipping them with both the analytical and cloud engineering skills required for professional AWS ML certification.

Agus Salim: Agus Salim is an experienced IT solutions and cybersecurity professional with a strong foundation in cloud infrastructure and project management. With over a decade of experience in systems integration, software development, and IT security across both enterprise and consulting environments, he brings a practical understanding of secure system design and deployment. His credentials include PMP, CompTIA Security+, CEH, and AWS Certified Cloud Practitioner, reflecting his balanced expertise in governance, risk management, and cloud operations. Agus has worked with leading organizations such as Citi and Check Point Software Technologies, providing hands-on technical and security support across multi-cloud platforms.

In the AWS Certified Machine Learning Specialty Training course, Agus emphasizes secure cloud implementation for AI and ML systems. He trains participants to integrate security-by-design principles into AWS-based data pipelines and machine learning workflows. His sessions focus on end-to-end ML lifecycle management—from data preparation and model training to monitoring and governance—ensuring learners are equipped to deploy AI solutions that meet industry and compliance standards.

Dr. Alfred Ang: Dr. Alfred Ang is a distinguished AI and digital transformation leader with over 20 years of experience in advanced computing, machine learning, and workforce development. As Chief Instructional Designer, Chief Technology Officer, and Chief Information Officer of Tertiary Infotech Pte Ltd, he has developed more than 500 WSQ- and IBF-accredited courses, bridging technical depth with industry-aligned training. He holds a PhD from the National University of Singapore, Master’s degrees from NTU, and an MBA from U21 Global, complemented by certifications including AWS Certified Machine Learning – Specialty, AWS AI Practitioner, AWS SysOps Administrator, and Microsoft Certified Azure AI Engineer. His expertise spans deep learning, NLP, computer vision, and cloud-based ML pipelines, reinforced by industrial projects such as robotic vision systems, agentic AI workflows, and multimodal AI platforms

As an ACLP- and DACE-certified curriculum developer, Dr. Ang has trained thousands of professionals in data science, AI, and cloud technologies, delivering courses for financial institutions, corporates, and government agencies. His teaching emphasizes hands-on, applied learning using AWS SageMaker, ML pipelines, and deployment strategies that prepare learners for the AWS Machine Learning Specialty certification. In addition to technical mastery, he integrates responsible AI and ethical considerations into his pedagogy, ensuring learners build scalable and trustworthy ML solutions. With his proven record of innovation, mentorship of interns from NUS, SIT, and NYP, and leadership in both industry and education, Dr. Ang is ideally positioned to guide participants in mastering AWS machine learning tools and certifications for real-world applications