Reverse Dependencies of azureml-core

The following projects have a declared dependency on azureml-core:

• ai-python — Microsoft AI Python Package
• amlctor — AML Pipeline Constructor
• andreani-aa-ml — Common functions for ML proyects used by the Andreani Advanced Analytics team
• arcus-azureml — A Python library to improve MLOps methodology on Azure Machine Learning
• automl-client-core-nativeclient — AutoML native client implementation
• autora-doc — Automatic documentation generator from AutoRA code
• azure-ml-component — Azure Machine Learning Component SDK
• azureml-accel-models — Used to create and train a model using various deep neural networks (DNNs).
• azureml-acft-accelerator — Contains the acft accelerator package used in script to build the azureml components.
• azureml-acft-common-components — Basic implementation of common components and utility functions that can be used by all verticals (image/video/nlp/multi-modal).
• azureml-acft-multimodal-components — Contains the acft multimodal-contrib package used in script to build azureml components.
• azureml-automl-dnn-nlp — End to end deep learning models for NLP tasks in AutoML.
• azureml-automl-dnn-vision — AutoML DNN Vision Models
• azureml-cli-common — (v1) Manage Azure Machine Learning resources. It is recommended to use the 2.0 extension - see aka.ms/azml2
• azureml-contrib-aisc — AzureML Contrib for AzureML AI Super Computer compute target. AISC Compute is a managed AI compute infrastructure.
• azureml-contrib-automl-dnn-forecasting — Azure Automated Machine Learning DNN package for timeseries forecasting.
• azureml-contrib-automl-dnn-vision — AutoML DNN Vision Models
• azureml-contrib-brainwave — Azure Machine Learning Hardware Accelerated models
• azureml-contrib-datadrift — Azure Machine Learning datadrift
• azureml-contrib-dataset — Contains experimental Dataset features for the azureml-core package.
• azureml-contrib-fairness — Uploads fairness dashboards to AzureML (preview).
• azureml-contrib-functions — Enable creation of Azure Functions applications from models registered with Azure Machine Learning.
• azureml-contrib-gbdt — azureml contrit gradient boosted decision tree
• azureml-contrib-iot — Azure Machine Learning IoT
• azureml-contrib-mir — AzureML Managed Inference Resource (MIRv1) Webservices client library
• azureml-contrib-notebook — no summary
• azureml-contrib-pipeline-steps — Azure Machine Learning Parallel Run Step - Deprecated
• azureml-contrib-reinforcementlearning — Provides preview feature for AzureML Python SDK that allows to submit Reinforcement Learning runs. NOTE: Service is being deprecated. See https://aka.ms/rldeprecation for more information and timelines.
• azureml-contrib-server — Local HTTP service used to expose the functionality given by the AzureML SDK to VS Tools for AI ext.
• azureml-contrib-tensorboard — no summary
• azureml-datadrift — Contains functionality for data drift detection for various datasets used in machine learning.
• azureml-defaults — Is a metapackage that is used internally by Azure Machine Learning
• azureml-designer-core — Core functionalities for data-type definition, data io and frequently-used functions.
• azureml-evaluate-mlflow — Contains the integration code of AzureML Evaluate with Mlflow.
• azureml-interpret — Machine Learning interpret package is used to interpret ML models
• azureml-metrics — Contains the ML and non-Azure specific common code associated with AzureML metrics.
• azureml-opendatasets — Provides a set of APIs to consume Azure Open Datasets.
• azureml-pipeline-core — Contains core functionality for Azure Machine Learning pipelines, which are configurable machine learning workflows.
• azureml-rag — Contains Retrieval Augmented Generation related utilities for Azure Machine Learning and OSS interoperability.
• azureml-responsibleai — AzureML Responsible AI package
• azureml-sdk — Used to build and run machine learning workflows upon the Azure Machine Learning service.
• azureml-synapse — Provides Magic command to manage Synapse session and submit job to Synapse Spark pool.
• azureml-telemetry — Used to collect telemetry data like Log messages, metrics, events, and activity messages
• azureml-tensorboard — Machine Learning TensorBoard package combines AzureML SDK with TensorBoard visualization
• azureml-train-automl-client — Used for automatically finding the best machine learning model and its parameters.
• azureml-train-automl-runtime — Used for automatically finding the best machine learning model and its parameters.
• azureml-train-core — azureml train core
• azuremlconstructor — AML Pipeline Constructor
• azuremlftk — "Microsoft Azure Machine Learning Forecasting Toolkit"
• cloud-data-connector — Intel's cloud data connector
• gordo — Train and build models for Argo / Kubernetes
• hi-ml-azure — Microsoft Health Futures package to elevate and monitor scripts to an AzureML workspace
• kreuzbergml — Toolbox for faster ML prototypes construction and demonstration.
• lmcmlflow — MLflow: A Platform for ML Development and Productionization
• marian-tensorboard — TensorBoard integration for Marian NMT
• Microsoft-AI-Azure-Utility-Samples — Utility Samples for AI Solutions
• mlflow-by-ckl — MLflow: A Platform for ML Development and Productionization
• mlflow-by-johnsnowlabs — MLflow: A Platform for ML Development and Productionization
• mlflow-by-johnsnowlabs-v2 — MLflow: A Platform for ML Development and Productionization
• mlflow-saagie — MLflow: A Platform for ML Development and Productionization - forked for Saagie
• mlflow-skinny — MLflow is an open source platform for the complete machine learning lifecycle
• mlflow-ste — MLflow: An ML Workflow Tool
• mlflow-tmp — MLflow: A Platform for ML Development and Productionization
• mlopsrobotics — no summary
• pre-ai-python — Microsoft AI Python Package
• prefect-azure — Prefect integrations with Microsoft Azure services
• projetaai-azure — Enables Azure services integration with ProjetaAi/Kedro
• pymarlin — Lightweight Deeplearning Library
• responsibleai-tabular-automl — SDK for computing RAI insights for AutoML models.
• sameproject — Notebooks to Pipelines, reproducible data science, oh my.
• shrike — Python utilities for compliant Azure machine learning
• TakeBlipPosTagger — PosTagger Package
• tdapiclient — Teradata API Client Python package