Technical Talks & Workshops

Apr 20, '25 | Build with AI, Universiti Teknologi Malaysia, Malaysia

This tech talk covers prompt engineering for generative AI, addressing challenges like output quality and ethical concerns. It offers strategies for effective prompting, including model understanding, structure, and techniques like Chain-of-Thought and Automatic Reasoning, and using Vertex AI for optimization.    [Slides]

Apr 10, '25 | IWD, Women Techmakers, Salzburg, Austria

This tech talk covers prompt engineering for generative AI, addressing challenges like output quality and ethical concerns. It offers strategies for effective prompting, including model understanding, structure, and techniques like Chain-of-Thought and Automatic Reasoning, and using Vertex AI for optimization.    [Slides]

Mar 04, '25 | IWD, Women Techmakers, Munich, Germany

We explore some advanced prompting strategies to unlock the real power for GenAI. This session explores how Vertex AI tools like the Model Garden, Prompt Gallery, and Prompt Optimizer enhance accuracy, efficiency, and cost-effectiveness through live demos and practical insights.    [Slides]

Dec 12, '24 | upGrad Institute, Singapore (Webinar)

This talk elaborates on the MLOps lifecycle for GenAI applications. It explores different services provided by Vertex AI, for discovery and experimentation, supervised tuning, model registration and deployments. We also touch upon model monitoring, model evaluation and grounding for ML applications. Learn more about the MLOps capabilities of Vertex AI by building your own philosopher chatbot that speaks in paradoxes.    [Slides]

Nov 24, '24 | GDG Devfest, Cebu, Philippines

This talk elaborates on the MLOps lifecycle for GenAI applications. It explores different services provided by Vertex AI, for discovery and experimentation, supervised tuning, model registration and deployments. We also touch upon model monitoring, model evaluation and grounding for ML applications. Learn more about the MLOps capabilities of Vertex AI by building your own philosopher chatbot that speaks in paradoxes.    [Slides]

Nov 23, '24 | GDG Devfest, Singapore: Gemini conference

This talk elaborates on the MLOps lifecycle for GenAI applications. It explores different services provided by Vertex AI, for discovery and experimentation, supervised tuning, model registration and deployments. We also touch upon model monitoring, model evaluation and grounding for ML applications. Learn more about the MLOps capabilities of Vertex AI by building your own philosopher chatbot that speaks in paradoxes.    [Slides]

Nov 05, '24 | Stack Developer Conference 2024, Singapore

This comprehensive workshop covers a detailed code walkthrough of building an AI agent from scratch and deploying it to production. We'll cover key concepts like RAG, and explore the design process for agents, including planning, tool design, handling agent communication, memory, evaluation, testing and observability. Attendees will get hands-on experience with both code and no-code tools to build and deploy AI agents to production using open source frameworks like LangChain and Cloud Platform tools like Vertex AI.    [Slides]

Sep 24, '24 | GDG, Singapore

Use GenAI for data cleaning, augmentation, and complex business analytics. This talk walks through GenAI capabilities for synthetic data generation, using multimodality for feature engineering and working with AI agents for data insights.    [Slides] [Demo]

Aug 30, '24 | Ernst & Young (EY), Malaysia (Webinar)

In this talk, we explore the topologies within different frameworks (LangGraph, CrewAI, MetaGpt and Autogen) and their approach to intra-agent communication. We'll delve into the strengths and weaknesses of each approach, offering a comprehensive perspective on building effective multi-agent systems.    [Slides]

Jul 26, '24 | upGrad Institute, Singapore (Webinar)

Leverage GenAI for data cleaning, augmentation, and complex business analytics. This talk walks through GenAI capabilities for synthetic data generation, using multimodality for feature engineering and working with AI agents for data insights.    [Slides] [Demo]