DSSG x Google: Building Generative AI applications with Langchain

With the widespread enthusiasm surrounding Generative AI and Large Language Models (LLMs), this inclusive 3-part workshop, designed for both novice and experienced AI engineers will get you building generative AI applications faster than you can say L-L-M. Pizzas will be provided (Thank you, Google!)

Agenda
6.30pm - 7.00pm: Registration
7.00pm - 7.45pm: Part I & II
7.45pm - 8.30pm: Part III
8.30pm - 9.00pm: Q&A

Part I: Introduction to Langchain
Ivan will kick off with an introduction to the core langchain framework – Models, Data connectivity, Chains, Agents, Memory all packaged in a Jupyter notebook to get you started quickly, along with prompt engineering guides and resources.

Part II: Langchain demo, challenges and takeaways
Next, we will showcase: (i) design process behind an AI Chemistry tutor using LangChain as a framework, challenges of building the AI tutor, workarounds, lessons learnt by Kwang Wen and (ii) Daniel’s to walkthrough Langflow for visualizing workflows, along with tips and tricks to building fast POCs.

Part III: Advanced use cases and autonomous agents
Martin will discuss "Self-Improvement" in the context of LLM/Agent systems : For instance, how these systems can make use of self-criticism and self-testing to generate new responses that lead to better outcomes. Sam's talk is titled "A Quick Demonstration of LangChain for Advanced Autonomous Agents", and he will show some very brief examples of the uses of router systems, combined agents and advanced LangChain patterns for building Autonomous Agents with LangChain.

Speaker Bio(s):
#1
Ivan oversees and coordinates a wide array of recurring events at DataScience SG. As the Head of Data at Tech in Asia, he leads a cross-functional data science, engineering and analytics team to build and transform functional departments into data-driven powerhouses. With over half a decade of experience working in the thriving Asian startup ecosystem, he is also an independent advisor to several AI startups.

#2
Kuang Wen is currently an educator who likes to tinker with code and build applications to bring tools to enhance teaching and learning in and out of class, as well as to decrease the workload of fellow educators.

#3
Daniel is a full-time NSF. Ever since picking up practical machine learning at the end of J1, he has written several articles published in Towards Data Science, as well as conducted a class in RI on the fast.ai library. He has won bronze in a Kaggle competition, and has been using Langchain to develop LLM applications in edtech since Jan ‘23. He is part of String, a tech discovery platform for public officers. He is passionate about applying ML in sports science and education.

#4
Martin has a PhD in Machine Learning, and has been an Open Source advocate since 1999. After a career in finance (based in London and New York), he decided to follow his original passion, and now works on Machine Learning / Artificial Intelligence full-time in Singapore, as the Head of AI at Red Dragon AI. Martin is co-organiser of the Machine Learning Singapore MeetUp group and is a regular speaker at events in the region.

#5
Sam has extensive experience in startups and mobile applications and is helping developers and companies create smarter applications with machine learning. He is especially passionate about Deep Learning and AI in the fields of Natural Language and conversational agents. Sam has used LangChain from close to its inception and his YouTube channel includes 20+ videos that the official LangChain site lists in their LangChain tutorials section. Along with being a co-organizer of ML Singapore, he is also one of the mentors for the Google for Startups program in Asia and North America.