Remove Data Pipeline Remove Data Preparation Remove Decision Trees
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2024 Mexican Grand Prix: Formula 1 Prediction Challenge Results

Ocean Protocol

2nd Place: Yuichiro “Firepig” [Japan] Firepig created a three-step model that used decision trees, linear regression, and random forests to predict tire strategies, laps per stint, and average lap times. Yunus focused on building a robust data pipeline, merging historical and current-season data to create a comprehensive dataset.

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Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

Understanding the MLOps Lifecycle The MLOps lifecycle consists of several critical stages, each with its unique challenges: Data Ingestion: Collecting data from various sources and ensuring it’s available for analysis. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning.

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.