Skip to content
#

model-training-and-tuning

Here are 22 public repositories matching this topic...

Beamline is a tool for fast data generation for your AI/LLM/ML model training, simulation, and testing use-cases. It generates reproducible pseudo-random data using a stochastic approach and probability distributions, meaning you can create realistic datasets that follow specific mathematical patterns.

  • Updated Aug 10, 2026
  • Rust

The goal of this project is to build a machine learning model to predict customer churn for Camtel 📡, a major telecommunications provider. Customer churn—the rate at which customers leave the service—is a key challenge that directly impacts revenue 💸 and business stability 🏢.

  • Updated Nov 17, 2024
  • Python

The **AWS SageMaker + Snowflake ML Pipeline** is a fully production-grade, end-to-end machine learning workflow designed to ingest large-scale data from Snowflake, perform feature engineering with Apache Spark, and train, tune, and deploy models on AWS SageMaker—all orchestrated and versioned with CI/CD, Terraform, and Ansible.

  • Updated Sep 1, 2026
  • Python

Titanic Survival Prediction Using Decision Tree. This project uses a Decision Tree Classifier to predict Titanic passenger survival based on the Kaggle dataset. It covers data preprocessing, feature engineering, and model training with Scikit-learn.

  • Updated May 4, 2025
  • Jupyter Notebook

Add this topic to your repo

To associate your repository with the model-training-and-tuning topic, visit your repo's landing page and select "manage topics."

Learn more