model='gemma3:latest' created_at='2025-09-18T08:10:14.5117975Z' done=True done_reason='stop' total_duration=23367861186 load_duration=606200532 prompt_eval_count=160 prompt_eval_duration=71905999 eval_count=1451 eval_duration=18459765790 response='```html\n\n\n\n \n \n Azure Machine Learning SDK for JavaScript - Documentation\n \n \n\n\n\n

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Azure Machine Learning SDK for JavaScript

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Introduction

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The Azure Machine Learning SDK for JavaScript allows you to build, train, and deploy machine learning models using JavaScript. It provides a seamless experience for working with Azure Machine Learning services, including Azure Machine Learning Compute, Azure Machine Learning Models, and Azure Machine Learning Pipelines.

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Key Features

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Model Training: Train custom machine learning models using various algorithms and frameworks.
Model Deployment: Deploy trained models to Azure Machine Learning Compute instances.
Pipelines: Build and execute machine learning pipelines for automated workflows.
Integration with Azure Services: Seamlessly integrate with other Azure services, such as Azure Storage and Azure Data Lake Storage.

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Getting Started

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To get started with the Azure Machine Learning SDK for JavaScript, follow these steps:

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Install the SDK: Use npm to install the SDK: `npm install azure-ai-ml`
Initialize the SDK: Create an instance of the SDK.
Connect to your Azure Machine Learning Workspace: Configure the SDK to connect to your workspace.
Start building your machine learning workflows!

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Code Examples

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\n              \n                import { MLClient } from "azure-ai-ml";\n\n                async function run() {\n                  try {\n                    const mlClient = new MLClient("YOUR_AZURE_SUBSCRIPTION_ID", "YOUR_RESOURCE_GROUP_NAME", "YOUR_WORKSPACE_NAME");\n\n                    const result = await mlClient.jobs.createOrUpdate(\n                      {\n                        displayName: "My Job",\n                        nodePoller: {\n                          timeout: 300\n                        },\n                        scoringNodeProfile: {\n                          vmSize: "Standard_DS3_v2"\n                        },\n                        scoringInput: {\n                          // Define your input data here\n                        }\n                      }\n                    );\n\n                    console.log("Job created with ID: " + result.id);\n\n                  } catch (error) {\n                    console.error("Error creating job:", error);\n                  }\n                }\n\n                run();\n              \n

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\n\n \n\n\n\n```' thinking=None context=[105, 2364, 107, 3048, 659, 496, 4108, 5941, 236761, 562, 2864, 815, 1164, 2229, 528, 580, 506, 2479, 965, 1356, 26223, 236786, 52078, 236786, 72198, 236786, 32876, 236786, 18770, 236786, 1389, 236772, 1528, 236786, 41213, 236772, 26661, 236761, 107, 118904, 607, 16167, 236786, 42970, 236786, 21083, 600, 563, 6780, 573, 600, 2479, 236761, 107, 16904, 994, 506, 16167, 236786, 42970, 236786, 21083, 236764, 5017, 1663, 236761, 107, 6294, 711, 3204, 1027, 8968, 653, 46119, 236761, 107, 27455, 531, 1386, 506, 4108, 3104, 618, 4148, 532, 10828, 618, 2653, 236761, 107, 8993, 236789, 236745, 4928, 600, 672, 563, 496, 16496, 236764, 1386, 625, 618, 21179, 618, 2653, 236761, 107, 3048, 740, 1138, 6658, 9851, 768, 6780, 236764, 840, 768, 506, 2430, 5238, 496, 6658, 3205, 236764, 611, 236789, 859, 735, 531, 6060, 600, 2311, 236764, 834, 577, 2889, 531, 1786, 3487, 4403, 573, 5869, 531, 3050, 1144, 611, 236789, 500, 3490, 528, 506, 10739, 236761, 106, 107, 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