Does it include patterns for data preparation?

Absolutely. It covers Spark-based feature engineering via ADF Data Flows and best practices for archiving ephemeral SQL data to Parquet in ADLS Gen2.

Does this skill support the latest Azure branding and platform changes?

Yes, it includes documentation for the transition from Azure AI Foundry to Microsoft Foundry as of November 2025 and uses updated API standards.

Can I use this for Large Language Model (LLM) integrations?

Yes, it contains patterns for the Azure OpenAI Batch API, which allows for text analysis and enrichment at 50% less cost than real-time endpoints.

How does this help with the Azure ML SDK v1 deprecation?

It provides specific implementation patterns and REST-based WebActivity examples to migrate from published pipelines to SDK v2 batch endpoints before June 2026.

Azure ML & Analytics Patterns for ADF

Name: Azure ML & Analytics Patterns for ADF
Author: JosiahSiegel

byJosiahSiegel

•

Data Science & ML

Orchestrates end-to-end machine learning and AI workflows within Azure Data Factory and Microsoft Foundry.

This skill provides specialized guidance for integrating Azure Data Factory with machine learning services, including critical migration paths from legacy Azure ML SDK v1 to modern SDK v2 batch endpoints. It offers implementation patterns for Azure OpenAI Batch APIs, Databricks ML training, and automated data archival from SQL to storage, ensuring that Claude can assist in building scalable, secure, and cost-optimized AI orchestration pipelines following the latest 2026 platform standards.

Key Features

01Migration guidance for Azure ML SDK v1 to SDK v2 batch endpoints

0224 GitHub stars

03End-to-end Databricks ML job integration and feature engineering

04Automated SQL-to-Storage archival for long-term ML data retention

05Orchestration patterns for Azure OpenAI Batch API and Microsoft Foundry

06Best practices for secure managed identity and cost-optimized scoring

Use Cases

01Designing automated data archival and feature engineering pipelines for Databricks model training

02Migrating legacy Azure ML pipelines to modern batch endpoints before the June 2026 support end date

03Implementing low-cost LLM text classification and enrichment using the Azure OpenAI Batch API

Key Features

01Migration guidance for Azure ML SDK v1 to SDK v2 batch endpoints

0224 GitHub stars

03End-to-end Databricks ML job integration and feature engineering

04Automated SQL-to-Storage archival for long-term ML data retention

05Orchestration patterns for Azure OpenAI Batch API and Microsoft Foundry

06Best practices for secure managed identity and cost-optimized scoring

Use Cases

01Designing automated data archival and feature engineering pipelines for Databricks model training

02Migrating legacy Azure ML pipelines to modern batch endpoints before the June 2026 support end date

03Implementing low-cost LLM text classification and enrichment using the Azure OpenAI Batch API