Introduction to MLflow with a demo locally and how to set it on AWS
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Updated
Mar 7, 2021 - Jupyter Notebook
Introduction to MLflow with a demo locally and how to set it on AWS
This repository provides an example of dataset preprocessing, GBRT (Gradient Boosted Regression Tree) model training and evaluation, model tuning and finally model serving (REST API) in a containerized environment using MLflow tracking, projects and models modules.
MLflow is Open source platform for the machine learning lifecycle so here you can learn MLflow End to End Example with Prediction.
Mlflow Docker Image
MLFlow End to End Workshop at Chandigarh University
Classifying asteroids based on NASA JPL data records
An end-to-end machine learning (mlops) project
Online Prediction Machine Learning System designed, deployed and maintained with MLOps Practices. Goal of the project is to predict individuals income based on census data.
Kubeflow Pipeline along with MLflow Tracking on a time series forecasting example.
Experiment tracking with MLFlow.
TechCon Experimentation with MLFlow and Dask
Using a stack of powerful tools to build an End-to-End AutoML pipeline for insurance cross-sell prediction
Training a YOLOv8 model for wildfire smoke detection.
Intent Classification with Hugging Face, Mlfow experiment tracking, Behavioural testing of models with checklist
Comparing performance of a small transformer model with and without Knowledge Distillation
Airflow Pipeline for Lead Scoring to Maximize Profit with retraining pipeline and Development experimentation using mlflow
Some examples of running R in a Docker container with machine learning and MLOps features
This is an end-to-end animal face classification model with Keras, KerasTuner, Mlflow, SQLite, Streamlit, and FastAPI which can classify animal faces as either cat, dog or wildlife
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