MLOS is a Data Science powered infrastructure and methodology to democratize and automate Performance Engineering. MLOS enables continuous, instance-based, robust, and trackable systems optimization.
Обновлено 2024-11-07 03:53:37 +03:00
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
machine-learning
data-science
causal-inference
causality
treatment-effects
bayesian-networks
causal-machine-learning
causal-models
do-calculus
graphical-models
python3
Обновлено 2024-11-06 18:53:00 +03:00
Best Practices on Recommendation Systems
azure
microsoft
machine-learning
python
deep-learning
kubernetes
data-science
artificial-intelligence
jupyter-notebook
tutorial
operationalization
ranking
rating
recommendation
recommendation-algorithm
recommendation-engine
recommendation-system
recommender
Обновлено 2024-11-01 16:20:38 +03:00
This package features data-science related tasks for developing new recognizers for Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models.
machine-learning
deep-learning
nlp
natural-language-processing
privacy
ner
transformers
pii
named-entity-recognition
spacy
flair
Обновлено 2024-10-27 15:51:02 +03:00
Python package for graph statistics
Обновлено 2024-10-09 19:41:04 +03:00
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
machine-learning
deep-learning
data-science
python
tensorflow
mlops
pytorch
hyperparameter-optimization
hyperparameter-tuning
machine-learning-algorithms
model-compression
nas
neural-architecture-search
neural-network
automated-machine-learning
automl
bayesian-optimization
deep-neural-network
distributed
feature-engineering
Обновлено 2024-07-03 13:54:08 +03:00
Quickstart template as a fork on TDSP (https://github.com/Azure/Azure-TDSP-ProjectTemplate), extending the template with a suggested structure for operationalization using Azure. Includes ARM templates as IaC for resource deployment, template build and release pipelines to enable model CI/CD, template code for working with Azure ML.
Обновлено 2023-03-28 19:45:00 +03:00
GitHub Action that allows you to deploy machine learning models in Azure Machine Learning.
Обновлено 2021-10-19 10:13:54 +03:00
GitHub Action that allows you to register models to your Azure Machine Learning Workspace.
Обновлено 2021-10-19 10:13:21 +03:00
GitHub Action that allows you to submit a run to your Azure Machine Learning Workspace.
Обновлено 2021-10-19 10:12:45 +03:00
GitHub Action that allows you to attach, create and scale Azure Machine Learning compute resources.
Обновлено 2021-10-19 10:12:10 +03:00
GitHub Action that allows you to create or connect to your Azure Machine Learning Workspace.
Обновлено 2021-10-19 10:11:09 +03:00
Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.
Обновлено 2020-07-17 00:00:07 +03:00
This repository contains instructions and code to deploy a customer 360 profile solution on Azure stack using the Cortana Intelligence Suite.
azure
azure-functions
data-science
decision-trees
feature-engineering
customer-enrichment
data-virtualization
datawarehouse
multi-class-classification
Обновлено 2018-08-10 20:22:24 +03:00