Creating an end-to-end machine learning pipeline, implementing experiment tracking with MLflow, and performing hyperparameter optimization using Optuna.
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Updated
Jun 6, 2024 - Python
Creating an end-to-end machine learning pipeline, implementing experiment tracking with MLflow, and performing hyperparameter optimization using Optuna.
Honing Professional ML skills by solving Kaggle Competitions using ML tools from Google Cloud and ML Best Practices. Analyzing the Winner Solutions.
A versatile Python application using Streamlit for hands-on experience in programming and machine learning. OptiML-Analyzer enables qualitative and quantitative data analysis using various machine learning algorithms through a user-friendly interface.
The Anonymous Synthesizer for Health Data
An open-source ML pipeline development platform
Repo containing Channel Quality Indicator (CQI) data from real car routes in Greece. It contains a reproducable notebook with the implementation of a Bidirectional LSTM Neural Network for real-time CQI forecasting in heterogeneous ultra-dense beyond-5G networks.
Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production
This Vertex AI Pipeline orchestrates the selection, deployment, and real-time monitoring of the highest-performing machine learning model from a pool of candidates. Designed to support a dynamic and collaborative model development environment, it ensures that only the most accurate and relevant models are deployed for fraud detection tasks.
This machine learning pipeline project aims to develop an ML model to identify bank customer churn.
Multi-Label Classifier with ETL, NLP, Sklearn, Flask, + Plotly
⛰️ machine learning pipeline for disaster alert
Opt-Out tool to check Copyright reservations in a way that even machines can understand.
Student performance prediction
This application classifies messages using Random Forest.
This project, in collaboration with Figure Eight as part of Udacity's Data Science Nanodegree program, focuses on real-time message categorization for disaster events. It involves an ETL pipeline, ML pipeline, and Web app for classifying disaster response messages.
Develoment of a machine learning model optimizing telemarketing through prediction of marketing calls that don't lead to customer conversion
From data gathering to model deployment. Complete ML pipeline using Docker, Airflow and Python.
📅 A demo about versioning data and tracking ML experiments using DVC and Mlflow respectively.
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