你可以在本地计算机或远程虚拟机上设置环境,例如 Azure 机器学习计算实例或 Data Science VM。 若要配置本地开发环境或远程 VM,请执行以下操作: 创建Python 虚拟环境(virtualenv,conda)。 备注 虽然不需要,但建议使用Anaconda或Miniconda来管理 Python 虚拟环境并安装包。
Azure CLI 可使用mltable Python SDK创建 MLTable,如以下示例所示: Python importmltable paths = [ {'file':'./train_data/bank_marketing_train_data.csv'} ] train_table = mltable.from_delimited_files(paths) train_table.save('./train_data') ...
- Training 使用Python SDK v2 为 Azure 机器学习中的模型创建和探索负责任 AI 仪表板。 认证 Microsoft Certified: Azure Data Scientist Associate - Certifications 使用Python、Azure 机器学习和 MLflow 管理数据引入和准备、模型训练和部署以及机器学习解决方案监视。
Azure Machine Learning 文件 概觀 設定 快速入門 開始使用 Azure 機器學習 教學課程 從基本概念著手 建置模型 受控功能存放區 與Azure 機器學習 互動 使用資料 自動化 Machine Learning 概念 操作方式 針對表格式資料使用自動化 ML (Python, CLI) 使用自動化 ML (介面) ...
Bring your machine learning models to market faster Simplify the way you build and deploy models with no-code automated machine learning capabilities, open-source support, and robust DevOps for machine learning. Try Azure Machine Learning
Bring your machine learning models to market faster Simplify the way you build and deploy models with no-code automated machine learning capabilities, open-source support, and robust DevOps for machine learning. Try Azure Machine Learning
python 数据分析模块（Numpy、Scipy、Scikit和Pandas等） python进行机器学习(tensorflow） 一、基础包 ①...
A data scientist wants to experiment by training a machine learning model and tracking it with Azure Machine Learning. Which tool should be used to train the model by running a script from their preferred environment? how the experiments can be tracked using the python sdk. It would be easy ...
Welcome to the Azure Machine Learning Python SDK notebooks repository! Getting started These notebooks are recommended for use in an Azure Machine LearningCompute Instance, where you can run them without any additional set up. However, the notebooks can be run in any development environment with the...
from azureml.core.compute import ComputeTarget, ComputeInstance # Use the name of your current instance compute_name = 'docs-ci' # Confirm your current instance instance = ComputeInstance.create(ws, compute_name, compute_config) instance.wait_for_completion(show_output=True) ...