ml-ops

Prefect is a workflow orchestration framework for building resilient data pipelines in Python.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

A curated collection of publicly available resources on how technology and tech-savvy organizations around the world practice Site Reliability Engineering (SRE)

A curated list of articles that cover the software engineering best practices for building machine learning applications.

An open-source ML pipeline development platform

Fire up your models with the flame 🔥

A Collection of GitHub Actions That Facilitate MLOps

Azure Databricks MLOps sample for Python based source code using MLflow without using MLflow Project.

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.

Designing IT and ML Applications using Systems Thinking Approach at IIT Bhilai (CS559)

Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]

Curated examples and patterns for using Chalk. Use these to build your feature pipelines.

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.

Vehicle data classification (supervised, unsupervised learning)

A ready to use architecture for processing data and performing machine learning in Azure

The universal data connector