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machine-learning-operations

Here are 12 public repositories matching this topic...

EthicalML / awesome-production-machine-learning

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

machine-learning data-mining awesome deep-learning awesome-list interpretability privacy-preserving production-machine-learning mlops privacy-preserving-machine-learning explainability responsible-ai machine-learning-operations ml-ops ml-operations privacy-preserving-ml large-scale-ml production-ml large-scale-machine-learning

Updated Jun 16, 2022

bentoml / BentoML

The Unified Model Serving Framework 🍱

kubernetes machine-learning ai deep-learning aws-lambda tensorflow ml pytorch model-management model-deployment model-serving ml-infrastructure azure-ml mlops aws-sagemaker machine-learning-operations bentoml ml-platform prediction-service

Updated Jun 23, 2022
Python

SeldonIO / seldon-core

Open

Enhance Inference Graph Documentation

1

mwm5945 commented Jun 25, 2020

The documentation for creating complex inference graphs should include what components can be connected to what, and how the overall graph should behave.

For example, all inference graphs need to end with a single node, whether its a combiner combining multiple inputs, or a transformer, or a model--the graph can't split and then never rejoin, etc.

Read more

good first issue v2

evidentlyai / evidently

Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

data-science machine-learning pandas-dataframe jupyter-notebook html-report production-machine-learning mlops model-monitoring machine-learning-operations data-drift

Updated Jun 23, 2022
Python

mattborghi / mlops-specialization

Machine Learning Engineering for Production (MLOps) Coursera Specialization

data-science data machine-learning deep-learning coursera neural-networks coursera-specialization mlops machine-learning-operations

Updated May 22, 2021
Jupyter Notebook

patternex / awesome-ml-for-threat-detection

A curated list of resources to deep dive into the intersection of applied machine learning and threat detection.

machine-learning cybersecurity awesome-list papers threat-detection applied-machine-learning machine-learning-systems machine-learning-operations

Updated Sep 23, 2020

neuro-inc / neuro-extras

Curated set of MLOps tools to work with the Neu.ro MLOps platform

neuro mlops machine-learning-operations

Updated Jun 22, 2022
Python

kostaleonard / mlops

A framework for conducting MLOps.

machine-learning deep-learning machine-learning-operations

Updated Apr 16, 2022
Python

DrazenDodik / tf2_quickstart_valohai

In this tutorial we'll bring the TensorFlow 2 Quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.

tensorflow valohai machine-learning-tutorial machine-learning-operations

Updated May 26, 2022
Python

dpanigra / solutions

Cloud Solutions for machine learning and operation.

machine-learning cloud retail kubeflow-pipelines machine-learning-operations kfserving

Updated Nov 19, 2020
Scala

abhisrn1986 / TwitterDataPipeline

The project comprises a real-time tweets data pipeline, a sentimental analysis of the tweets module, and a Slack bot to post the tweets' sentiments. The project uses SentimentIntensityAnalyzer from the VaderSentiment library. The analyzer gives positive, negative, and compound scores for small texts (such as tweets in this case). The real-time data pipeline flow is as follows: 1.Tweets are collected and stored in a database. 2.The sentiment of the tweets is analyzed. 3.The tweet sentiment is posted on a Slack channel using a Slack bot.

python slack docker data-science mongodb tweets sentiment-analysis pipeline slackbot docker-compose docker-image tweepy vader-sentiment-analysis data-pipeline replicaset shell-scripting machine-learning-engineering tweepy-api docker-file machine-learning-operations

Updated Jun 10, 2022
Python

awesome-mlops / awesome-mlops-kubernetes

A curated list of awesome open source tools and commercial products that will help you train, deploy, monitor, version, scale, and secure your production machine learning on kubernetes 🚀

kubernetes workflow machine-learning deep-learning ml orchestration dataops operator machine-learning-pipelines model-management model-training machine-learning-workflow machine-learning-projects machine-learning-training mlops ml-pipeline model-monitoring machine-learning-operations ml-workbench

Updated Apr 20, 2022

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