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19 search results found
Machinelearningnotebooks
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3,832
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
Forecasting
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2,540
Time Series Forecasting Best Practices & Examples
Azureml Examples
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1,442
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
Mlops
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1,427
MLOps examples
Mlops V2
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393
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Hi Ml
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210
HI-ML toolbox for deep learning for medical imaging and Azure integration
Azureml Sdk For R
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106
Azure Machine Learning SDK for R
Mlops Templates
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57
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Aireferencearchitectures
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56
Official Azure Reference Architectures for AI workloads
Azureml Cheatsheets
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25
Azure Machine Learning Cheat Sheets
Datacamp
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24
@Microsoft Data Camp (Analytics with Azure Machine Learning)
Distributeddeeplearning
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24
Distributed Deep Learning using AzureML
Azure Machine Learning Mlops Workshop
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23
A workshop for doing MLOps on Azure Machine Learning
Mldevops
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20
ML DevOps using GitHub Actions and Azure Machine Learning
Az Ml Batch Score
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18
Deploying a Batch Scoring Pipeline for Python Models
Azure Automl Mlops
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13
Get started with Automated Machine Learning (AutoML) and Machine Learning Operations (MLOps) in Azure Machine Learning
Innereye Gateway
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12
The InnerEye-Gateway is a Windows service that acts as a DICOM end point to run inference on https://github.com/microsoft/InnerEye-DeepLearning models.
Amlworkshop Iotedge Devops
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10
This repo has some proposed agenda for Azure Machine Learning related hands-on workshops.
Aml Airfield
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8
Ready to use scoring engines for Image, Text and Time Series
Dasky
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6
Azure ML + Dask = OSS Data Science & ML @ Scale
Digital Foundry Demand Forcasting
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5
In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must
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