Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines
-
Updated
Jun 13, 2024 - Python
Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines
An open-source ML pipeline development platform
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
A simple example on how to provide ML model (DecissionTreeClassifier) as a REST Service. The app is containerize and deployed in Azure Cloud
Fire up your models with the flame 🔥
Find the samples, in the test data, on which your (generative) model makes mistakes.
This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.
A library of computer vision models and a streamlined framework for training them.
A prefect extension that builds on top of the task decorator to reduce negative engineering!
Efficient streaming data ingestion, transformation & activation
A pipeline to CI/CD of a machine learning model on Google Cloud Run
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
Demo usage of Weights & Biases for ML Ops
Repo for running Whylogs as part of a CI workflow using github actions.
A simple Python example of a Model Service that can be fronted by the Model Sidecar
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
Add a description, image, and links to the ml-ops topic page so that developers can more easily learn about it.
To associate your repository with the ml-ops topic, visit your repo's landing page and select "manage topics."