Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
An open-source ML pipeline development platform
Fire up your models with the flame 🔥
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
A pipeline to CI/CD of a machine learning model on Google Cloud Run
Efficient streaming data ingestion, transformation & activation
Find the samples, in the test data, on which your (generative) model makes mistakes.
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
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 prefect extension that builds on top of the task decorator to reduce negative engineering!
Repo for running Whylogs as part of a CI workflow using github actions.
Demo usage of Weights & Biases for ML Ops
A library of computer vision models and a streamlined framework for training them.
A simple Python example of a Model Service that can be fronted by the Model Sidecar
Automated Data Scientist: An intelligent, adaptive data analysis tool that leverages AI-driven automation to dynamically plan, execute, and refine data science workflows. Automatically handles data preparation, analysis planning, code generation, and result interpretation using advanced language models.
A simple example on how to provide ML model (DecissionTreeClassifier) as a REST Service. The app is containerize and deployed in Azure Cloud
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