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 data framework for biology. Makes your data queryable, traceable, reproducible, and FAIR. One API: lakehouse, lineage, feature store, ontologies, LIMS, ELN.
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
Find the samples, in the test data, on which your (generative) model makes mistakes.
Efficient streaming data ingestion, transformation & activation
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]
A complete machine-learning system that predicts AI assistant user satisfaction using behavioral signals such as device, usage category, time features, session metrics, and model metadata. Includes full ML pipeline, SHAP explainability, evaluation suite, and an interactive Streamlit analytics dashboard.
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)
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