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CSA Loom — the Microsoft Fabric experience for Azure tenants where Fabric isn't yet available: lakehouses, warehouses, notebooks, semantic models, Activator rules, Data Agents, across Commercial, GCC, GCC-High, and DoD IL5

Data Science parity

What Fabric does

Fabric Data Science = Notebook + ML Model + ML Experiment + ML Job item types. MLflow fully integrated (experiment tracking, model registry). SynapseML preinstalled. AI Functions library exposes GPT-class operations as Spark DataFrame functions. Semantic Link + semantic-link-labs let notebooks read/write Power BI semantic models programmatically. "Prep for AI" is a semantic-model-authoring UI for encoding AI instructions, verified answers, and schema annotations consumed by Data Agents.

CSA Loom parity design

Notebooks

Covered in Data Engineering parity — Databricks notebooks via Loom Console Notebook pane.

MLflow + Model Registry

Boundary Implementation
Commercial / GCC Databricks-managed MLflow (once UC managed Gov-GAs in v1.1) — registry, experiment tracking, model serving
GCC-High / IL4 / IL5 OSS MLflow on AKS (mlflow server container with Postgres backend + ADLS Gen2 artifact store)

Loom Console "Models" pane lists registered models, versions, and stages. Backed by MLflow REST API.

SynapseML

Available in Databricks notebooks via PyPI install — no SynapseML SaaS feature gap.

AI Functions library

Custom Python library apps/fiab-ai-functions/ packaging the same DataFrame-level APIs Fabric exposes:

from fiab_ai_functions import sentiment, summarize, classify, translate, embed

# Same call shape as Fabric's AI Functions
df_with_sentiment = df.transform(sentiment("review_text"))

Each function wraps an AOAI call. Available as PyPI install in any Databricks notebook. Configured via the Console "Admin → AI Settings" pane (endpoint, model deployment, TPM allocation).

semantic-link-labs (open-source, Microsoft-maintained) reads Power BI semantic models via XMLA endpoint. Works against Power BI Premium directly without a Fabric-specific dependency. Documented in Tutorial 03 — Direct Lake parity.

"Prep for AI" parity

Per-table + per-column annotations stored in Cosmos DB and surfaced by Loom Console's Semantic Model designer. Loom Data Agents reads these annotations as part of the system-prompt grounding.

Model Serving

Boundary Implementation
Commercial / GCC (post UC managed Gov-GA) Databricks Model Serving
GCC-High / IL4 / IL5 Azure ML managed online endpoints OR AKS-hosted MLflow serving with custom inference image

Loom Console "Endpoints" pane surfaces both deployment paths.

Boundary Implementation
Commercial / GCC (post UC managed Gov-GA) Databricks Vector Search
GCC-High / IL4 / IL5 Azure AI Search vector + integrated vectorization (authorized through IL6 per research/02-gov-boundary-availability.md §7.9)

Per-boundary behavior

Boundary Managed MLflow Vector Search Model Serving
Commercial ✅ Databricks (when UC GA) ✅ Databricks ✅ Databricks
GCC ✅ Databricks (when UC GA) ✅ Databricks ✅ Databricks
GCC-High / IL4 ❌ OSS on AKS ❌ Azure AI Search ❌ Azure ML / AKS
IL5 (v1.1) ❌ OSS on AKS ❌ Azure AI Search ❌ Azure ML / AKS

Honest gaps

  • Databricks Vector Search and Model Serving aren't in Gov today; Azure AI Search + Azure ML are the substitutions
  • AI Foundry portal isn't at IL4/IL5; use classic Azure ML Hub (Microsoft.MachineLearningServices/workspaces) in Gov

Forward migration

  • MLflow experiments + models export via mlflow's portable JSON format → Fabric MLflow
  • Notebooks via Git
  • Vector indexes via re-embed (no zero-copy path; Vector embeddings are model-specific)