Part I: Ryan Siegler, Data Scientist, KX: GenAI, RAG, and Vector Databases
Part II: Saeed Amen, Founder, Cuemacro: Speeding up Python with financial use cases
Part III: The seminar will conclude with a Q&A Faculty session. Moderator: Paul Bilokon
Panellists: Jack Jacquier, Saeed Amen and Abir Sridi.
This is a joint event hosted by The Machine Learning Institute Certificate in Finance (MLI) https://mlinstitute.org/ and The Quantitative Developer Certificate (QDC) https://qdc.org.uk
Live Venue: Level39, One Canada Square, Canary Wharf, London E14 5AB
Event Timings:
Doors Open (live in-person): 17.30
Presentation and Q&A Starts (live and online): 18.00 – 19.00
BTRM networking drinks / snacks reception & London Chapter: 19.00 – 20.30
Emerging technologies, such as ChatGPT and other LLMs, other types of artificial intelligence, and quantum computing are disrupting all industries, finance included.
Knowing AI is no longer a career-furthering option for financial professionals, it’s a vital necessity in the present-day competitive environment.
In order to help current and aspiring quantitative analysts and developers transition to an AI-enabled world, we have created two certification programmes: the Machine Learning Institute Certificate in Finance (MLI) and Quantitative Developer Certificate (QDC).
This event will enable you to meet some of the faculty members, other industry leaders, and fellow delegates, inform you about the latest industry trends, and enable you to ask questions about the MLI and QDC.
Part I: Ryan Siegler, Data Scientist, KX: GenAI, RAG, and Vector Databases
Financial technologists are being challenged to incorporate Generative AI into their workflows, particularly when workflows involve, say, proprietary data and market data in addition to unstructured data, such as Twitter feeds or analyst reports. Terms like RAG (Retrieval Augment Generation) and “vector database” are littering desks that engage with AI and analytics. In this session, Ryan gives a background into the recent AI explosion and the new lexicon, and in particular highlights the vector database as a repository of stored enterprise knowledge. He will demonstrate several examples that use contextual intelligence sourced from unstructured data using the KDB.AI vector database.
Part II: Saeed Amen, Founder, Cuemacro: Speeding up Python with financial use cases
Python is the programming language of choice for AI and data science, but it is hardly the fastest. Can we speed it up?
In this talk, Saeed Amen looks at many different approaches for speeding up code with examples from financial markets.
We consider using Dask and Vaex instead of Pandas, with tick data. We use Numba to speed up generic Python code, such as moving averages. We compare the advantages of Numba with those of Cython. Finally, we use various tricks of the trade to speed up the computation of the CDF.
We conclude that even though Python is not fast out of the box, we can speed it up considerably. Python’s days are not Numba-ed!
