#other #applied_data_science #applied_machine_learning #data_engineering #data_science #machine_learning #nlp #papers #production #recommendation #recsys #reinforcement_learning #search

https://github.com/eugeneyan/applied-ml

#python #asr #conformer #e2e_models #production_ready #pytorch #transformer

https://github.com/mobvoi/wenet

#other #devops #availability #list #awesome #monitoring #reliability_engineering #incident_response #site_reliability_engineering #production #post_mortem #capacity_planning #service_level_agreement #scalability #reliability #alerting #on_call #awesome_list #sre #postmortem #site_reliability

https://github.com/dastergon/awesome-sre

#html #aiops #deployment #kubernetes #machine_learning #machine_learning_operations #mlops #production_machine_learning #serving

https://github.com/SeldonIO/seldon-core

#go #acl #authorization #authzed #ciam #cloud_native #database #distributed #distributed_systems #fine_grained_access_control #graph_database #grpc #kubernetes #latency #permissions #production #scale #security #security_tools #spicedb #zanzibar

https://github.com/authzed/spicedb

#python #data_drift #data_science #hacktoberfest #html_report #jupyter_notebook #machine_learning #machine_learning_operations #mlops #model_monitoring #pandas_dataframe #production_machine_learning

https://github.com/evidentlyai/evidently

#python #ai #automl #data_science #deep_learning #devops_tools #hacktoberfest #llm #llmops #machine_learning #metadata_tracking #ml #mlops #pipelines #production_ready #pytorch #tensorflow #workflow #zenml

https://github.com/zenml-io/zenml

#other #awesome #awesome_list #data_mining #deep_learning #explainability #interpretability #large_scale_machine_learning #large_scale_ml #machine_learning #machine_learning_operations #ml_operations #ml_ops #mlops #privacy_preserving #privacy_preserving_machine_learning #privacy_preserving_ml #production_machine_learning #production_ml #responsible_ai

This repository provides a comprehensive list of open-source libraries and tools for deploying, monitoring, versioning, scaling, and securing machine learning models in production. Here are the key benefits The repository includes a wide range of tools categorized into sections such as adversarial robustness, agentic workflow, AutoML, computation load distribution, data labelling and synthesis, data pipelines, data storage optimization, data stream processing, deployment and serving, evaluation and monitoring, explainability and fairness, feature stores, and more.

- **Production Readiness** The repository is actively maintained and contributed to by a community of developers, ensuring that the tools are up-to-date and well-supported.

- **Ease of Use** Tools for optimized computation, model storage optimization, and neural search and retrieval help in improving the performance and efficiency of machine learning models.

- **Privacy and Security**: Libraries focused on privacy and security, such as federated learning and homomorphic encryption, ensure that sensitive data is protected during model training and deployment.

Using this repository, you can streamline your machine learning workflows, improve model performance, and ensure robustness and security in your production environments.

https://github.com/EthicalML/awesome-production-machine-learning

#python #ansible #aws #azure #coding #containers #devops #docker #git #interview #interview_questions #kubernetes #linux #openstack #production_engineer #prometheus #python #sql #sre #terraform

This repository contains a collection of exercises and questions on various technical topics, including DevOps and SRE. It offers 2624 exercises that can be useful for preparing for interviews or learning new concepts. The repository covers a wide range of subjects such as networking, operating systems, cloud computing, and more. By using these resources, you can improve your skills in areas like software development, infrastructure management, and system reliability engineering. This helps you become more proficient in handling complex IT environments and enhances your career prospects in related fields.

https://github.com/bregman-arie/devops-exercises

#python #asr #automatic_speech_recognition #conformer #e2e_models #production_ready #pytorch #speech_recognition #transformer #whisper

WeNet is a powerful tool for speech recognition that helps turn spoken words into text. It's designed to be easy to use and works well in real-world situations, making it great for businesses and developers. WeNet provides accurate results on many public datasets and is lightweight, meaning it doesn't require a lot of resources to run. This makes it beneficial for users who need reliable speech-to-text functionality without complex setup or maintenance.

https://github.com/wenet-e2e/wenet