Setting up an MLflow Workspace with Docker
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Updated
Aug 23, 2024 - Jupyter Notebook
Setting up an MLflow Workspace with Docker
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Experiment tracking with MLFlow.
The MLflow TensorFlow Guide is an educational project. This project demonstrates how to build, train, and manage a TensorFlow machine learning model using MLflow, a powerful open-source platform for the end-to-end machine learning lifecycle.
Host MLFlow Tracking Server and Model Registry as a containerized application on Kubernetes
Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq
🌐 Language identification for Scandinavian languages
In this project, you will create an end-to-end Airflow pipeline, integrated with MLflow, for CodePro, an EdTech startup, to perform lead scoring and maximize profitability while minimizing the Customer Acquisition Cost (CAC). The assignment involves data collection, preprocessing, and periodic model retraining within Airflow, alongside development
A hands-on MLflow project demonstrating experiment tracking, model training, and lifecycle management using Scikit-learn, XGBoost, and Dagshub integration.
Practical project for time series prediction using recurrent neural networks (RNN) with Python.
Link to DagsHub repository :
Add a description, image, and links to the mlflow-ui topic page so that developers can more easily learn about it.
To associate your repository with the mlflow-ui topic, visit your repo's landing page and select "manage topics."