Data Science Milan #003
Data Science Milan
The Community of Data Scientists and Machine Learning Practitioners based in the Greater Milan area.
Dear Data Science Milan Community,
Welcome back to our newsletter, bringing you another edition packed with the latest developments, inspiring projects, and invaluable insights from the world of data science!
The data science community doesn't stop even in the summer and we want to highlight the Euroscipy 2023 conference held in August at Basel, providing the YouTube channel with the recorded talks and workshops.
There were covered wide topics and we would like to point out some workshops such as Introduction to Geospatial Machine Learning with SRAI, Network Analysis Made Simple (and fast!), some interesting talks like Anomaly Detection in Time Series: Techniques, Tools and Tricks, GPT generated text detection: problems and solution in the scientific publishing, the keynote talk by Ritchie Vink about Polars, and much more.
Data?Science?Milan?events
Building GenAI applications with LangChain on GCP
On June 27th, 2023, Ivan Nardini from Google Italia, Ivan Vigorito and Domenico Vitarella from Generali showed us an interesting Generative AI use case. ?
With an introduction from Ivan about LangChain, a framework that simplifies 'prompt piping' with large language models (LLMs) using Google Cloud. Then Ivan and Domenico shared a case study of using LangChain with PALM 2 for knowledge base Q&A in Generali. The magic is done by giving PALM 2 the ability to query a 'chunkenized' version of different documents using LangChain to 'connect' the information that is not available together.
TinyML
In this talk Pietro Montino spoke about TinyML, a core technology of modern IoT systems where data are analyzed close to where they are collected,?with benefits for energy efficiency, latency, privacy, and cost.
Explainable AI in the Banking Industry - Application of DevOps and MLOps towards digital automation in banking use cases
In the first talk Ilaria Bordino, discussed explainable AI in the banking industry, emphasizing the need for AI systems that amplify human capabilities and incorporate ethical principles. She mentioned popular methods like LIME and SHAP for model explainability and introduced the concept of counterfactual explanations.
In the second talk, Bhaskar Chakraborty explained the challenges faced in developing ML applications for automating banking workflows, such as document classification and information extraction. Bhaskar emphasized the role of DevOps in addressing these challenges by automating the continuous integration (CI) and continuous delivery (CD) processes. He explored the ML lifecycle, including data ingestion, cleaning, validation, model development, and serving.?
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Knowledge section
Here are selected resources about Causal Inference:
-Lecture notes of Causal Inference from University of California Berkeley - A First Course in Causal Inference
The recorded streaming from The Causal AI Conference 2023 - cAI23
A repository of video lectures and a tutorial about Machine Learning and Causal Inference released by Stanford University - Machine Learning & Causal Inference: A Short Course | Stanford Graduate School of Business
Be involved!
We want also to remind you that if you like and enjoy our events, you can get in touch with us at?[email protected]?to be involved in organizing new great online activities.
We are also very happy if you are interested in being a speaker or if you want to share your expertise or experience with the?Data?Science?Milan?community!!!
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