← Products

Data
βš™ Needs: an Azure subscription with billed Azure Data Factory…

Azure Data Factory ML & Analytics Patterns β€” orchestrate ML workflows with ADF

πŸ’³ Paid cloud required β€” orchestrate machine learning with Azure Data Factory: Azure ML batch endpoints (SDK v2), Azure OpenAI Batch API, Databricks ML, SQL-to-Storage archival, REST connector recipes β€” includes March 2026 deprecation notices (SDK v1 ends June 2026)

At a glance
What
πŸ’³ Paid cloud required β€” orchestrate machine learning with Azure Data Factory: Azure ML batch endpoints (SDK v2), Azure OpenAI Batch API, Databricks ML, SQL-to-Storage archival, REST connector recipes β€” includes March 2026 deprecation notices (SDK v1 ends June 2026)
Cost
Free
Needs
an Azure subscription with billed Azure Data Factory, Azure Machine Learning, and (optionally) Databricks usage β€” no usable free tier; familiarity with ADF pipelines
Install
Copy the installer prompt below into your Muse β€” your agent does the rest.

Version:

@
Created by: @josiahsiegel
⌁

Install

Prerequisites: an Azure subscription with billed Azure Data Factory, Azure Machine Learning, and (optionally) Databricks usage β€” no usable free tier; familiarity with ADF pipelines Install "Azure Data Factory ML & Analytics Patterns β€” orchestrate ML workflows with ADF" for me. It gives my agent @josiahsiegel's ADF ML pattern library: Azure ML batch endpoints via WebActivity (SDK v2, migrated off SDK v1), Azure OpenAI Batch API pipeline patterns, ADF ML scoring orchestration, SQL-to-Storage archival pipelines, AI Services integration via REST connector, Databricks notebook execution from ADF, Data Flow feature engineering, and Synapse/Fabric integration β€” with a pattern quick-reference, security best practices (managed identity, Key Vault, never hardcode secrets), cost optimization, and links to detailed reference JSON. MIT licensed. Repository: https://github.com/josiahsiegel/claude-plugin-marketplace/blob/main/plugins/adf-master/skills/adf-ml-analytics/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code β€” secrets via Key Vault and managed identity only. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "adf-ml-analytics". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. verify the deprecation dates against live Azure docs β€” the March 2026 notices will go stale; confirm my Azure subscription has the ML and Databricks resources; never put real keys in pipeline JSON). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.

?

Questions

How do I install a build?

Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you β€” no manual configuration.

Where does my money go?

Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.

What does the βœ“ next to a creator’s name mean?

It means we confirmed the identity of the person behind the listing. It says nothing about the code itself β€” always check a build before installing it.