refactor: update repository name to integratedml-custom-models

Updated all references to reflect new repository name and branding:

- Repository name: integratedml-flexible-model-integration → integratedml-custom-models
- Feature name: IntegratedML Flexible Model Integration → IntegratedML Custom Models
- Description: Updated to emphasize custom models capability and control

Changes:
- README.md: Enhanced overview to position as new IntegratedML capability
- .env.example: Updated header and experiment tracking project names
- .github/REPOSITORY_SETUP.md: Updated social media templates, topics, URLs
- docker-compose.yml: Updated header comment
- Makefile: Updated header and help message
- pyproject.toml: Updated description and keywords

The new name better reflects the actual feature name (IntegratedML Custom
Models) and is more concise and searchable.

Author: tom-dyar

.env.example

@@ -1,9 +1,9 @@
 # =============================================================================
-# IntegratedML Flexible Model Integration Environment Configuration
+# IntegratedML Custom Models Environment Configuration
 # =============================================================================
-# 
+#
 # This file serves as a template for environment variables required by the
-# IntegratedML Flexible Model Integration project. Copy this file to .env and fill in 
+# IntegratedML Custom Models project. Copy this file to .env and fill in
 # the actual values.
 #
 # SECURITY NOTE: Never commit .env files with real secrets to version control.
@@ -172,11 +172,11 @@ ENABLE_PROFILING=false
 
 # Optional: MLflow tracking (for experiment management)
 # MLFLOW_TRACKING_URI=http://localhost:5000
-# MLFLOW_EXPERIMENT_NAME=IntegratedML_Flexible_Model_Integration
+# MLFLOW_EXPERIMENT_NAME=IntegratedML_Custom_Models
 
 # Optional: Weights & Biases (for experiment tracking)
 # WANDB_API_KEY=your-wandb-api-key
-# WANDB_PROJECT=integratedml-flexible-model-integration
+# WANDB_PROJECT=integratedml-custom-models
 
 # =============================================================================
 # CI/CD Configuration

.github/REPOSITORY_SETUP.md

@@ -1,6 +1,6 @@
 # Repository Setup Guide for GitHub Publication
 
-This guide provides templates and recommendations for optimizing the IntegratedML Flexible Model Integration repository for maximum discoverability and community engagement.
+This guide provides templates and recommendations for optimizing the IntegratedML Custom Models repository for maximum discoverability and community engagement.
 
 ## 📝 Repository Description Template
 
@@ -25,7 +25,7 @@ Database-integrated ML demos: 4 enterprise examples showcasing IntegratedML plug
 - `intersystems-iris`
 - `machine-learning`
 - `database-ml`
-- `flexible-model-integration`
+- `custom-models`
 - `scikit-learn`
 - `python`
 
@@ -67,7 +67,7 @@ Database-integrated ML demos: 4 enterprise examples showcasing IntegratedML plug
 
 ### LinkedIn Post Template
 ```
-🚀 New open-source project: IntegratedML Flexible Model Integration Demo
+🚀 New open-source project: IntegratedML Custom Models Demo
 
 ✅ 4 progressive ML demos (Credit Risk → DNA Analysis)
 ✅ 67ms average latency, 95.4% accuracy validated
@@ -78,24 +78,24 @@ Perfect for learning database-integrated ML workflows!
 
 #MachineLearning #InterSystems #OpenSource #MLOps #DataScience
 
-https://github.com/intersystems-community/integratedml-flexible-model-integration
+https://github.com/intersystems-community/integratedml-custom-models
 ```
 
 ### Twitter/X Post Template
 ```
-🧬 New: IntegratedML Flexible Model Integration Demo
+🧬 New: IntegratedML Custom Models Demo
 
 4 enterprise ML examples:
 🏦 Credit Risk (Beginner)
-🚨 Fraud Detection (67ms!)  
+🚨 Fraud Detection (67ms!)
 📈 Sales Forecasting
 🧬 DNA Analysis
 
 B+ code quality, full CI/CD, <100ms predictions
 
 #ML #OpenSource #DataScience
 
-https://github.com/intersystems-community/integratedml-flexible-model-integration
+https://github.com/intersystems-community/integratedml-custom-models
 ```
 
 ### Reddit Post Ideas
@@ -235,12 +235,12 @@ Enterprise-ready ML demos for IntegratedML: 4 progressive examples from credit r
 
 ### Topics (Comma-separated)
 ```
-integratedml, intersystems-iris, machine-learning, database-ml, flexible-model-integration, scikit-learn, python, credit-risk-assessment, fraud-detection, sales-forecasting, dna-analysis, enterprise-ml, production-ml, intersystems-community, open-source, demo-project, tutorial, best-practices
+integratedml, intersystems-iris, machine-learning, database-ml, custom-models, scikit-learn, python, credit-risk-assessment, fraud-detection, sales-forecasting, dna-analysis, enterprise-ml, production-ml, intersystems-community, open-source, demo-project, tutorial, best-practices
 ```
 
 ### Website URL
 ```
-https://intersystems-community.github.io/integratedml-flexible-model-integration/
+https://intersystems-community.github.io/integratedml-custom-models/
 ```
 
 ---

Makefile

@@ -1,11 +1,11 @@
-# IntegratedML Flexible Model Integration - Development Automation
+# IntegratedML Custom Models - Development Automation
 # Simplified workflow for IRIS + local development
 
 .PHONY: help setup start stop clean install test format lint notebooks
 
 # Default target
 help:
-	@echo "IntegratedML Flexible Model Integration - Available Commands:"
+	@echo "IntegratedML Custom Models - Available Commands:"
 	@echo ""
 	@echo "Setup & Environment:"
 	@echo "  setup     - Complete project setup (dependencies + IRIS)"

README.md

@@ -25,7 +25,7 @@
 
 ## Overview
 
-IntegratedML Custom Models allows you to deploy custom Python models directly within SQL queries. This feature enables in-database machine learning without data movement, making it easier to integrate ML models into existing database workflows.
+**IntegratedML Custom Models** extends InterSystems IRIS IntegratedML with a powerful new capability: deploy your own Python models directly within SQL queries. While IntegratedML has provided automated ML for years, this feature gives data scientists full control—custom preprocessing, any scikit-learn compatible model, and third-party libraries like Prophet or LightGBM—all executing in-database without data movement.
 
 ```sql
 -- Train your custom Python model with a single SQL command

docker-compose.yml

@@ -1,4 +1,4 @@
-# IntegratedML Flexible Model Integration - Simplified Docker Setup
+# IntegratedML Custom Models - Simplified Docker Setup
 # IRIS database only - users run notebooks in VS Code locally
 
 version: '3.8'

pyproject.toml

@@ -5,20 +5,20 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "integratedml-demos"
 version = "1.0.0"
-description = "Comprehensive demo portfolio showcasing IntegratedML's flexible model integration capability"
+description = "Comprehensive demo portfolio showcasing IntegratedML Custom Models capability"
 readme = "README.md"
 license = {text = "MIT"}
 authors = [
     {name = "InterSystems Corporation", email = "support@intersystems.com"},
 ]
 keywords = [
-    "integratedml", 
-    "machine learning", 
-    "database", 
-    "sql", 
+    "integratedml",
+    "machine learning",
+    "database",
+    "sql",
     "scikit-learn",
     "ensemble models",
-    "flexible model integration",
+    "custom models",
     "intersystems"
 ]
 classifiers = [