ml-ops
There are 40 repositories under ml-ops topic.
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
PrefectHQ/prefect
Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines
SE-ML/awesome-seml
A curated list of articles that cover the software engineering best practices for building machine learning applications.
sematic-ai/sematic
An open-source ML pipeline development platform
vortico/flama
Fire up your models with the flame 🔥
machine-learning-apps/actions-ml-cicd
A Collection of GitHub Actions That Facilitate MLOps
Azure-Samples/azure-databricks-mlops-mlflow
Azure Databricks MLOps sample for Python based source code using MLflow without using MLflow Project.
MatsMoll/aligned
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
jgvaraujo/ml-deployment-on-gcloud
A pipeline to CI/CD of a machine learning model on Google Cloud Run
NucleusEngineering/hack-your-pipe
Efficient streaming data ingestion, transformation & activation
guidelabs/infembed
Find the samples, in the test data, on which your (generative) model makes mistakes.
gagan-iitb/ComputerSysDesign
Designing IT and ML Applications using Systems Thinking Approach at IIT Bhilai (CS559)
gasparian/ml-serving-template
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
ziss11/heart-failure-detection
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
lfunderburk/fuel-electric-hybrid-vehicle-ml
Vehicle data classification (supervised, unsupervised learning)
AdamPaternostro/Azure-Big-Data-and-Machine-Learning-Architecture
A ready to use architecture for processing data and performing machine learning in Azure
chalk-ai/examples
Curated examples and patterns for using Chalk. Use these to build your feature pipelines.
marwan116/supreme-task
A prefect extension that builds on top of the task decorator to reduce negative engineering!
spoke-data/spoke
The universal data connector
whylabs/whylogs_action
Repo for running Whylogs as part of a CI workflow using github actions.
aws-samples/amazon-sagemaker-pipeline-deploy-manage-100x-models-python-cdk
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.
kayvane1/wandb-course
Demo usage of Weights & Biases for ML Ops
Quantyca/content-qshare
Raccogliamo qui tutti i link alle risorse menzionate durante i nostri QShare
Ideas2IT/datascience-lab
Data Science Experiments Repository of Ideas2IT
jasmeetsb/airflow_ml_ops
Sample Airflow ML Pipelines
ml-starter-packs/microservice-workshop
interactive coding environment for microservices demo
theNewFlesh/flatiron
A library of computer vision models and a streamlined framework for training them.
ameron-ai/model-serving-sidecar-service-example
A simple Python example of a Model Service that can be fronted by the Model Sidecar
mariagrandury/is-your-ml-model-trustworthy
Slides of my talk "Is Your ML Model Trustworthy?" at the MLOps World Conference on the 16th of June 2021.
rafka-imandaputra/ORBIT-TSDN-2022
ORBIT SMKN 4 Bandung team repository for Turnamen Sains Data Nasional 2022 coordinated by Cybertrend Data Academy and Asosiasi Data Sains dan AI Indonesia with supported by several government agencies and universities in Indonesia.
vortico/flama-demos
Examples showcasing Flama 🔥
a-ngo/ml-ops
These are my personal notes regarding Machine Learning DevOps stuff
MGTheTrain/ml-ops-poc
Repository showcasing ML Ops practices with kubeflow and mlflow
nikofebrianur/Bank-Customer-Churn-Prediction
This machine learning pipeline project aims to develop an ML model to identify bank customer churn.
uday160386/ml-model-as-rest-api
A simple example on how to provide ML model (DecissionTreeClassifier) as a REST Service. The app is containerize and deployed in Azure Cloud