Brembo Hackathon<span style="font-family: Arial, sans-serif; color: rgb(68, 108, 179); font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">The Hackathon <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Join us in Sunnyvale, California on October 13 – 15th for the Brembo Hackathon: Data Science, Machine Learning and GenAI Solutions Challenge, where you will be hacking on Machine Learning, Data Science and Generative AI challenges that utilize historical data sets and exclusive industry access from the global leader in brake production.<span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">At the heart of the hackathon lies a vast and diverse dataset provided by Brembo. This dataset encompasses a wealth of information on various car models, their braking systems, performance metrics, and real-world driving scenarios. Armed with this invaluable data, participants will be tasked with building innovative machine learning models capable of predicting and optimizing brake designs, or, solving warranty database issues to further improve Brembo’s already in-place model.<span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Participants will choose between one of two hackathon challenges which are defined below. Each challenge will have it’s own unique data set, sample modeling, and will have a workshop/webinar to be held <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">1-2 weeks before the hackathon to explain how the data is structured. Register today and don’t miss out on this exciting opportunity to work with automotive experts and transform the next generation of Machine Learning and AI with Brembo! <span style="font-family: Arial, sans-serif; color: rgb(68, 108, 179); font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">The Challenges: <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">1. Gen AI & Brake Pad Recipe Creation <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Using friction test data provided by Brembo, use Generative AI to create new compounds, forecast testing results and create the framework for predicting the effectiveness and characteristics of a new Brembo brake product. The data provided will include a list of compounds previously used and tested by Brembo, as well as their outcomes. Solutions must be based on Generative AI, applied to provide a model able to propose new recipes that increase the number of candidate compounds, ensuring feasibility and good performances. <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">2. Synthetic Data for Warranty Classification <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Based on a historical Warranty Database, which includes a set of claims from customers, generate synthetic data that can augment Brembo’s data set, retrain the current production model, and determine if there is improvement in the final classifier. The synthetic data that is generated should resemble the data that Brembo is providing, and should be used to improve Brembo’s existing model. <span style="font-family: Arial, sans-serif; color: rgb(68, 108, 179); font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Judging Criteria & Submission Requirements: <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">There will be separate submission requirements and criteria for Challenge 1 and Challenge 2. We will explain on-site during the hackathon the process for submitting and testing your solutions early as well. <span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Challenge 1: Gen AI & Brake Pad Recipe Creation <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Submission Requirements: <ol style="margin-top:0;margin-bottom:0;padding-inline-start:48px;"><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Dataset of proposed compounds in the std. format provided by Brembo</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Presentation Deck  </li></ol><span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Brembo will then evaluate the proposed compounds and give feedback to the teams in terms of: <ol style="margin-top:0;margin-bottom:0;padding-inline-start:48px;"><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Technical Feasibility: Validation of proposed compounds against production constraints.</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Technical Relevance: Comparison of test conditions (specific columns) against reality.</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Target Performance: Comparison of the proposed compound’s performance against the target.</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Compounds Variability: Computation of a variability index among all the proposed compounds.  </li></ol><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-weight: 700; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Challenge 2: Synthetic Data for Warranty Classification <span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Submission Requirements:  <ol style="margin-top:0;margin-bottom:0;padding-inline-start:48px;"><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Synthetic dataset in the std. format provided by Brembo</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Presentation Deck  </li></ol><span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">Brembo will then evaluate each team's submission and:  <ol style="margin-top:0;margin-bottom:0;padding-inline-start:48px;"><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Train the target model on the augmented dataset, obtaining a newly trained model.</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Compare performances of the newly-trained model with the target model on the test set.</li><li dir="ltr" style="list-style-type: decimal; font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space: pre;">Provide feedback to the teams in terms of mean per-class error.  </li></ol><span style="font-size: 14pt; font-family: Arial, sans-serif; color: rgb(65, 65, 65); font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; vertical-align: baseline; white-space-collapse: preserve;">The judges will then review all projects using the above criteria and tests to determine the winning teams!<div> </div>