Azure Data Factory ML & Analytics Patterns β orchestrate ML workflows with ADF
π³ 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)
- What
- π³ 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.
Curated by Skill Harbor β π³ Paid cloud required: this needs an Azure subscription with billed Data Factory, Machine Learning, and Databricks usage β no usable free tier. @josiahsiegel's adf-ml-analytics skill, listed here with credit to its creator: a pattern library for orchestrating ML workflows with Azure Data Factory β Azure ML batch endpoints via WebActivity (SDK v2, the migration path from the dying SDK v1), Azure OpenAI Batch API pipeline patterns (LLM scoring at half cost), ADF ML scoring orchestration, SQL-to-Storage archival pipelines (Parquet, watermark patterns), AI Services integration via REST connector, Databricks notebook execution from ADF, Data Flow feature engineering, and Synapse/Fabric integration. It ships a pattern quick-reference table with activity types, best practices (archive first, Parquet, managed identity, Key Vault, never hardcode secrets), cost-optimization guidance, and links into detailed reference files with complete activity JSON. Honest caveats: cloud documentation drifts fast β the March 2026 deprecation notices (Azure AI Foundry β Microsoft Foundry rename, Azure ML SDK v1 support ending June 2026, azure-ai-inference SDK retiring May 2026, Azure SQL Edge and Cognitive Services for Power BI retired) will go stale; verify dates against live docs; ADF classic receives fewer new features than Fabric Data Factory. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
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 sellerβs own page, usually Stripe.
What does the β next to a creatorβs 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.