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Leveraging Data Insights From Risk Management to Marketing Optimization
2024-10-24 · 16:00
It's been a while since the PyData Amsterdam 2024 conference, so it's time for another meetup! JetBrains Datalore and PyData Amsterdam are pleased to announce our first joint meetup. We have prepared several intriguing and practical talks from Adyen, ABN AMRO, and JetBrains. They will surely attract many involved in data analytics and LLMs. After the presentations, we will happily discuss these and other topics during the networking session. We invite everyone working in the fields of data science and data analysis. Hope to see you on October 24 at 18:00 at The Social Hub Amsterdam City, Wibautstraat 129, 1091 GL, Amsterdam. SCHEDULE
TALKS [Talk 1]: “MPLX - Machine Learning Driven Risk Management” by Rogier Verlinden & Jesse Koreman Adyen processed more than 900 billion euro’s in 2023. With some of these transactions becoming a chargeback or refund, we need a comprehensive and adaptive risk system to mitigate our financial exposure. In this talk we’ll explain what crucial role is played by machine learning in this system, how we ensure scalability and what lessons were learned along the way [Talk 2]: “Optimize your marketing expenses: An overview of different methods" by Bas Stinenbosch How effective is your marketing? This is a question that not a lot of companies are able to answer. However, with the right data science methods you will be able save money and boost your sales. In this talk I will give an overview of the different methods to gain insights and show you how to use them. So, if you want to learn more about Multi Touch Attribution modeling with Markov Chains or Marketing Mix Modelling with a Bayesian approach, then this talk is the one for you! [Talk 3]: “Lies\, damned lies and large language models" by Dr. Jodie Burchell Would you like to use large language models (LLMs) in your own project, but are troubled by their tendency to frequently “hallucinate”, or produce incorrect information? Have you ever wondered if there was a way to easily measure an LLM’s hallucination rate, and compare this against other models? And would you like to learn how to help LLMs produce more accurate information? In this talk, we’ll have a look at some of the main reasons that hallucinations occur in LLMs, and then focus on how we can measure one specific type of hallucination: the tendency of models to regurgitate misinformation that they have learned from their training data. We’ll explore how we can easily measure this type of hallucination in LLMs and end by looking at recent initiatives to reduce hallucinations in LLMs, using a technique called retrieval augmented generation (RAG). We’ll look at how and why RAG makes LLMs less likely to hallucinate, and how this can help make these models more reliable and usable in a range of contexts. |
Leveraging Data Insights From Risk Management to Marketing Optimization
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#132 The Past, Present, and Future, of the Data Science Notebook
2023-04-03 · 10:00
Dr. Jodie Burchell
– Data Science Developer Advocate
@ JetBrains
The concept of literate programming, or the idea of programming in a document, was first introduced in 1984 by Donald Knuth. And as of today, notebooks are now the defacto tool for doing data science work. So as the data tooling space continues to evolve at breakneck speed, what are the possible directions the data science notebook can take? In this episode of DataFramed, we talk with Dr. Jodie Burchell, Data Science Developer Advocate at JetBrains, to find out how data science notebooks evolved into what they are today, what her predictions are for the future of notebooks and data science, and how generative AI will impact data teams going forward. Jodie completed a Ph.D. in clinical psychology and a postdoc in biostatistics before transitioning into data science. She has since worked for 7 years as a data scientist, developing products ranging from recommendation systems to audience profiling. She is also a prolific content creator in the data science community. Throughout the episode, Jodie discusses the evolution of data science notebooks over the last few years, noting how the move to remote-based notebooks has allowed for the seamless development of more complex models straight from the notebook environment. Jodie and Adel’s conversation also covers tooling challenges that have led to modern IDEs and notebooks, with Jodie highlighting the importance of good database tooling and visibility. She shares how data science notebooks have evolved to help democratize data for the wider organization, the tradeoffs between engineering-led approaches to tooling compared to data science approaches, what generative AI means for the data profession, her predictions for data science, and more. Tune in to this episode to learn more about the evolution of data science notebooks and the challenges and opportunities facing the data science community today. Links to mentioned in the show: DataCamp Workspace: An-in Browser Notebook IDEJetBrains' DataloreNick Cave on ChatGPT song lyrics imitating his styleGitHub Copilot More on the topic: The Past, Present, And Future of The Data Science NotebookHow to Use Jupyter Notebooks: The Ultimate Guide |
DataFramed |