diff --git a/learn/custom_or_prebuilt_components.py b/learn/custom_or_prebuilt_components.py
new file mode 100644
index 0000000..011d194
--- /dev/null
+++ b/learn/custom_or_prebuilt_components.py
@@ -0,0 +1,120 @@
+"""
+Machine Learning Operations Playbook Adoption Workshop – Phase 2:
+Data Services Integration Architecture - Scavenger Hunt
+
+File: custom_or_prebuilt_components.py
+
+Purpose:
+--------
+This file provides scavenger hunt instructions for learners to explore
+existing AWS-based labs (Lab 6.1 and Lab 6.2) and plan migration to
+Vertex AI architecture. The focus is on Amazon S3 and Amazon Redshift
+integration patterns, and how to prepare to convert them into Vertex AI
+components using Kubeflow @component decorators.
+
+Learners should use VSCode/PyCharm search to locate the TODO markers
+listed below and record WHERE (line of code), WHAT (purpose), and WHY
+(rationale for migration). This stage is planning-only: code remains in
+its AWS state, but learners should envision how it will map to Vertex AI
+components.
+
+Target Vertex Architecture Structure:
+-------------------------------------
+├── src/
+│   ├── components/
+│   │   ├── __init__.py
+│   │   │
+│   │   ├── custom_data_quality_components.py      # ✅ Custom
+│   │   ├── custom_training_components.py          # ✅ Custom
+│   │   ├── custom_evaluation_components.py        # ✅ Custom
+│   │   ├── custom_registry_components.py          # ✅ Custom
+│   │   ├── custom_monitoring_components.py        # ✅ Custom
+│   │   ├── custom_audit_components.py             # ✅ Custom
+│   │   ├── custom_sysco_modelplaceholder_components.py        # ✅ Custom
+│   │   │
+│   │   └── prebuilt_bigquery_components.py        # ✅ Pre-built
+
+Scavenger Hunt Instructions:
+----------------------------
+
+1. Lab 6.1 — Amazon S3 Integration with SageMaker Workflows
+   - Search: "# TODO: Lab 6.1.1 - Line-by-Line Import Exploration"
+     * WHERE: Top of model.py imports
+     * WHAT: Identify boto3/joblib imports
+     * WHY: These libraries enable artifact persistence in S3
+     * Migration Planning: In Vertex AI, this logic would move into
+       custom_training_components.py with @component decorators, using
+       GCS (gs:// URIs) instead of S3.
+   - Search: "# TODO: Lab 6.1.4 - S3 Data Loading Conversion"
+     * WHERE: _s3_persist() function in model.py
+     * WHAT: Inspect boto3.upload_file usage
+     * WHY: Durable storage pattern in AWS
+     * Migration Planning: Replace with GCS client logic inside a
+       @component in custom_registry_components.py.
+
+   AWS Information to Gather for Migration:
+   - S3 bucket name (e.g., `my-ml-artifacts-bucket`)
+   - Bucket region (e.g., `us-east-1`)
+   - IAM role or access keys with `AmazonS3FullAccess`
+   - Artifact paths (prefixes like `s3://bucket/models/`)
+   - Current SageMaker registry integration points
+
+   Equivalent in GCP:
+   - GCS bucket name (e.g., `gs://my-ml-artifacts`)
+   - GCP project ID and region
+   - Service account with `Storage Admin` role
+   - Artifact paths in GCS (prefixes like `gs://bucket/models/`)
+
+2. Lab 6.2 — Amazon Redshift Data Pipeline and ML Integration
+   - Search: "# TODO: Lab 6.2.1 - Data Access Pattern Conversion"
+     * WHERE: ingest_model.py _read_from_redshift()
+     * WHAT: Inspect select_sql_from_dict or pd.read_sql usage
+     * WHY: Redshift → DataFrame conversion
+     * Migration Planning: Equivalent logic would move into
+       prebuilt_bigquery_components.py using BigQuery query components.
+   - Search: "# TODO: Lab 6.2.4 - Data Movement and Performance Considerations"
+     * WHERE: stage_table_to_s3() in ingest_model.py
+     * WHAT: Inspect UNLOAD vs client-side upload patterns
+     * WHY: Efficiency vs cost trade-offs in Redshift
+     * Migration Planning: Replace with BigQuery export jobs inside
+       prebuilt_bigquery_components.py or custom_data_quality_components.py.
+
+   AWS Information to Gather for Migration:
+   - Redshift cluster identifier (e.g., `redshift-cluster-1`)
+   - Database name (e.g., `analytics_db`)
+   - Schema names (e.g., `public`, `ml_features`)
+   - User credentials or IAM role with Redshift access
+   - Connection endpoint (host, port)
+   - Common SQL queries used for ETL (COPY, UNLOAD, CTAS)
+
+   Equivalent in GCP:
+   - BigQuery dataset name (e.g., `ml_features_dataset`)
+   - BigQuery table names (e.g., `training_data`, `evaluation_data`)
+   - GCP project ID and region
+   - Service account with `BigQuery Admin` role
+   - SQL queries adapted to BigQuery syntax (SELECT, CREATE TABLE AS)
+
+3. Planning Migration with Vertex Kubeflow @component Decorators
+   - For S3 → GCS:
+     * Wrap artifact persistence logic in @component functions inside
+       custom_training_components.py and custom_registry_components.py.
+     * Replace boto3 calls with google-cloud-storage client calls.
+   - For Redshift → BigQuery:
+     * Wrap ETL and query logic in @component functions inside
+       prebuilt_bigquery_components.py.
+     * Replace psycopg2/sqlalchemy calls with google-cloud-bigquery client
+       or prebuilt BigQuery components.
+
+Learner Deliverable:
+--------------------
+For each TODO marker found:
+- Record WHERE: file name and line of code
+- Record WHAT: the code pattern or component
+- Record WHY: the rationale for its use in AWS
+- Record Migration Plan: which Vertex component file it would map to
+  (custom_* or prebuilt_bigquery_components.py) with @component decorator
+
+This scavenger hunt prepares learners to design the component structure
+shown above by understanding the migration path from AWS (S3, Redshift)
+to Vertex AI (GCS, BigQuery, Pipeline components).
+"""
diff --git a/learn/hands_on_exercise.py b/learn/hands_on_exercise.py
deleted file mode 100644
index 19e8dcd..0000000
--- a/learn/hands_on_exercise.py
+++ /dev/null
@@ -1,202 +0,0 @@
-"""
-Ready?
-
-Review train.py and train_to_vertex_ai_conversion.py files for patterns.
-
-"""
-
-from kfp import dsl, components
-from kfp.dsl import (
-    component,
-    pipeline,
-    Input,
-    Output,
-    Model,
-    Metrics
-)
-from google_cloud_pipeline_components.types import artifact_types
-
-PIPELINE_NAME = "diabetes-classification-exercise-pipeline"
-BASE_IMAGE = "python:3.9"
-
-bigquery_query_job_op = components.load_component_from_url(
-    'https://us-kfp.pkg.dev/ml-pipeline/google-cloud-registry/'
-    'bigquery-query-job/sha256:'
-    'd1cae80bc0de4e5b95b994739c8d0d7d42ce5a4cb17d3c9512eaed14540f6343'
-)
-
-@component(
-    base_image=BASE_IMAGE,
-    packages_to_install=[
-        "google-cloud-bigquery",
-        "scikit-learn",
-        "joblib", 
-        "pandas"
-    ]
-)
-def evaluate_model_op(
-    test_data: Input[artifact_types.BQTable],
-    model: Input[Model],
-    metrics: Output[Metrics],
-    min_accuracy: float,
-    project_id: str,
-    bq_location: str
-) -> float:
-    import re, logging, joblib
-    import pandas as pd
-    from sklearn.metrics import accuracy_score
-    from google.cloud import bigquery
-
-    logging.basicConfig(level=logging.INFO)
-    
-    uri = test_data.uri
-    match = re.search(r'projects/([^/]+)/datasets/([^/]+)/tables/([^/]+)', uri)
-    if not match:
-        raise ValueError(f"Could not parse BQ table from URI: {uri}")
-    proj, dataset, table = match.groups()
-    table_ref = f"{proj}.{dataset}.{table}"
-
-    bq_client = bigquery.Client(project=project_id, location=bq_location)
-    query = f"SELECT * FROM `{table_ref}`"
-    test_df = bq_client.query(query).to_dataframe()
-
-    model_obj = joblib.load(model.path)
-    
-    FEATURE_COLUMNS = ["Pregnancies","PlasmaGlucose","DiastolicBloodPressure",
-                       "TricepsThickness","SerumInsulin","BMI","DiabetesPedigree","Age"]
-    X_test = test_df[FEATURE_COLUMNS]
-    y_test = test_df["Diabetic"]
-    
-    preds = model_obj.predict(X_test)
-    accuracy = accuracy_score(y_test, preds)
-
-    metrics.log_metric("accuracy", accuracy)
-    metrics.log_metric("test_samples", len(test_df))
-    
-    return accuracy
-
-@component(base_image=BASE_IMAGE)
-def model_approved_op(model_accuracy: float, model_name: str):
-    import logging
-    logging.basicConfig(level=logging.INFO)
-    logging.info("Model approved with accuracy: %.4f", model_accuracy)
-
-@component(
-    base_image=BASE_IMAGE,
-    packages_to_install=["google-cloud-aiplatform"]
-)
-def register_model_op(
-    project_id: str,
-    region: str, 
-    model_display_name: str,
-    model_artifact: Input[Model],
-    parent_model: str = ""
-):
-    from google.cloud import aiplatform
-    import logging
-    
-    logging.basicConfig(level=logging.INFO)
-    aiplatform.init(project=project_id, location=region)
-    
-    artifact_dir = model_artifact.uri.rsplit("/", 1)[0]
-    
-    upload_args = {
-        "display_name": model_display_name,
-        "artifact_uri": artifact_dir,
-        "serving_container_image_uri": "us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-2:latest",
-        "sync": True
-    }
-    
-    if parent_model:
-        upload_args["parent_model"] = parent_model
-    
-    model = aiplatform.Model.upload(**upload_args)
-
-@dsl.pipeline(
-    name=PIPELINE_NAME,
-    description="Exercise pipeline for train.py to Vertex AI conversion"
-)
-def diabetes_training_pipeline(
-    project_id: str,
-    region: str = "us-central1",
-    model_display_name: str = "diabetes-classification-model",
-    bq_dataset: str = "shared_bronze",
-    bq_view: str = "diabetes_features_view",
-    reg_rate: float = 0.01,
-    min_accuracy: float = 0.70,
-    parent_model: str = ""
-):
-    train_query = f"""
-    SELECT Pregnancies, PlasmaGlucose, DiastolicBloodPressure, TricepsThickness,
-           SerumInsulin, BMI, DiabetesPedigree, Age, Diabetic
-    FROM `{project_id}.{bq_dataset}.{bq_view}`
-    WHERE MOD(ABS(FARM_FINGERPRINT(CAST(CONCAT(Pregnancies, PlasmaGlucose) AS STRING))), 10) < 8
-    """
-    
-    test_query = f"""
-    SELECT Pregnancies, PlasmaGlucose, DiastolicBloodPressure, TricepsThickness,
-           SerumInsulin, BMI, DiabetesPedigree, Age, Diabetic
-    FROM `{project_id}.{bq_dataset}.{bq_view}`
-    WHERE MOD(ABS(FARM_FINGERPRINT(CAST(CONCAT(Pregnancies, PlasmaGlucose) AS STRING))), 10) >= 8
-    """
-    
-    bq_train_task = bigquery_query_job_op(
-        project=project_id, 
-        location=region, 
-        query=train_query
-    )
-    
-    bq_test_task = bigquery_query_job_op(
-        project=project_id, 
-        location=region, 
-        query=test_query
-    )
-    
-    train_task = train_model_op(
-        train_data=bq_train_task.outputs["destination_table"],
-        reg_rate=reg_rate,
-        project_id=project_id,
-        bq_location=region
-    ).set_cpu_limit("1").set_memory_limit("3840Mi")
-    train_task.after(bq_train_task)
-    
-    eval_task = evaluate_model_op(
-        test_data=bq_test_task.outputs["destination_table"],
-        model=train_task.outputs["output_model"],
-        min_accuracy=min_accuracy,
-        project_id=project_id,
-        bq_location=region
-    ).set_cpu_limit("1").set_memory_limit("3840Mi")
-    eval_task.after(train_task)
-    
-    with dsl.If(eval_task.outputs["Output"] >= min_accuracy, name="pass-accuracy-threshold"):
-        approved_task = model_approved_op(
-            model_accuracy=eval_task.outputs["Output"],
-            model_name=model_display_name
-        )
-        approved_task.after(eval_task)
-        
-        register_task = register_model_op(
-            project_id=project_id,
-            region=region,
-            model_display_name=model_display_name,
-            model_artifact=train_task.outputs["output_model"],
-            parent_model=parent_model
-        )
-        register_task.after(approved_task)
-
-    with dsl.If(eval_task.outputs["Output"] < min_accuracy, name="fail-accuracy-threshold"):
-        rejected_task = model_rejected_op(
-            model_accuracy=eval_task.outputs["Output"],
-            min_accuracy=min_accuracy
-        )
-        rejected_task.after(eval_task)
-
-if __name__ == "__main__":
-    from kfp import compiler
-    
-    compiler.Compiler().compile(
-        pipeline_func=diabetes_training_pipeline,
-        package_path="diabetes_training_exercise.json"
-    )
-    print("Pipeline compiled successfully!")
diff --git a/requirements.txt b/requirements.txt
index 6cacfa8..87422a3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,100 @@
-# This file points to the actual requirements in src/
-# All dependencies are managed in src/requirements.txt
--r src/requirements.txt
+# ============================================================================
+# Core ML & Data Science Libraries
+# ============================================================================
+# NumPy - Numerical computing (used by scikit-learn, pandas)
+numpy==1.23.5
+
+# Pandas - Data manipulation (used in all components for DataFrame operations)
+pandas==1.5.3
+
+# Scikit-learn - Machine learning (LogisticRegression, accuracy_score)
+scikit-learn==1.2.2
+
+# Joblib - Model serialization (save/load models as .joblib)
+joblib==1.2.0
+
+# ============================================================================
+# Google Cloud Platform Libraries
+# ============================================================================
+# Google Cloud Storage - GCS bucket operations (pipeline artifacts)
+google-cloud-storage==2.10.0
+
+# Google Cloud AI Platform - Vertex AI SDK (pipeline execution, model registry)
+google-cloud-aiplatform==1.56.0
+
+# Google Cloud BigQuery - BigQuery client (data reading in components)
+google-cloud-bigquery==3.11.4
+
+# BigQuery data types - Required for BigQuery DataFrame compatibility
+db-dtypes==1.1.1
+
+# PyArrow - Efficient data serialization for BigQuery
+pyarrow==12.0.1
+
+# ============================================================================
+# Kubeflow Pipelines (KFP) v2
+# ============================================================================
+# KFP SDK - Pipeline definition and component creation
+kfp==2.7.0
+
+# Google Cloud Pipeline Components - Pre-built GCP components (BigQuery Query Job)
+google-cloud-pipeline-components==2.14.1
+
+# ============================================================================
+# Testing Libraries (for tests/unit/)
+# ============================================================================
+# Pytest - Test framework
+pytest==7.4.3
+
+# Pytest-cov - Code coverage reporting
+pytest-cov==4.1.0
+
+# Pytest-mock - Enhanced mocking capabilities
+pytest-mock==3.12.0
+
+# ============================================================================
+# Code Quality & Linting (CI/CD)
+# ============================================================================
+# Flake8 - Python linting (enforces PEP8, detects errors)
+flake8==6.1.0
+
+# Black - Code formatter (consistent code style)
+black==23.12.1
+
+# isort - Import sorting (organized imports)
+isort==5.13.2
+
+# ============================================================================
+# YAML Processing (for governance/resource_auditor.py)
+# ============================================================================
+# PyYAML - YAML parsing (used in resource auditor for pipeline YAML)
+PyYAML==6.0.1
+
+# ============================================================================
+# Type Checking (Optional - for development)
+# ============================================================================
+# Mypy - Static type checking
+# mypy==1.7.1
+
+# ============================================================================
+# Documentation (Optional - for future Sphinx docs)
+# ============================================================================
+# Sphinx - Documentation generation
+# sphinx==7.2.6
+# sphinx-rtd-theme==2.0.0
+
+# ============================================================================
+# Version Compatibility Notes
+# ============================================================================
+# - All versions tested with Python 3.9
+# - Compatible with base_image="python:3.9" in KFP components
+# - google-cloud-aiplatform 1.56.0 supports KFP 2.7.0
+# - google-cloud-pipeline-components 2.14.1 requires KFP 2.7.0
+# - BigQuery libraries (google-cloud-bigquery, db-dtypes, pyarrow) work together
+# - Pytest versions compatible with Python 3.9
+#
+# Installation:
+#   pip install -r src/requirements.txt
+#
+# Update all packages (use with caution):
+#   pip install --upgrade -r src/requirements.txt
diff --git a/src/requirements.txt b/src/requirements.txt
deleted file mode 100644
index 87422a3..0000000
--- a/src/requirements.txt
+++ /dev/null
@@ -1,100 +0,0 @@
-# ============================================================================
-# Core ML & Data Science Libraries
-# ============================================================================
-# NumPy - Numerical computing (used by scikit-learn, pandas)
-numpy==1.23.5
-
-# Pandas - Data manipulation (used in all components for DataFrame operations)
-pandas==1.5.3
-
-# Scikit-learn - Machine learning (LogisticRegression, accuracy_score)
-scikit-learn==1.2.2
-
-# Joblib - Model serialization (save/load models as .joblib)
-joblib==1.2.0
-
-# ============================================================================
-# Google Cloud Platform Libraries
-# ============================================================================
-# Google Cloud Storage - GCS bucket operations (pipeline artifacts)
-google-cloud-storage==2.10.0
-
-# Google Cloud AI Platform - Vertex AI SDK (pipeline execution, model registry)
-google-cloud-aiplatform==1.56.0
-
-# Google Cloud BigQuery - BigQuery client (data reading in components)
-google-cloud-bigquery==3.11.4
-
-# BigQuery data types - Required for BigQuery DataFrame compatibility
-db-dtypes==1.1.1
-
-# PyArrow - Efficient data serialization for BigQuery
-pyarrow==12.0.1
-
-# ============================================================================
-# Kubeflow Pipelines (KFP) v2
-# ============================================================================
-# KFP SDK - Pipeline definition and component creation
-kfp==2.7.0
-
-# Google Cloud Pipeline Components - Pre-built GCP components (BigQuery Query Job)
-google-cloud-pipeline-components==2.14.1
-
-# ============================================================================
-# Testing Libraries (for tests/unit/)
-# ============================================================================
-# Pytest - Test framework
-pytest==7.4.3
-
-# Pytest-cov - Code coverage reporting
-pytest-cov==4.1.0
-
-# Pytest-mock - Enhanced mocking capabilities
-pytest-mock==3.12.0
-
-# ============================================================================
-# Code Quality & Linting (CI/CD)
-# ============================================================================
-# Flake8 - Python linting (enforces PEP8, detects errors)
-flake8==6.1.0
-
-# Black - Code formatter (consistent code style)
-black==23.12.1
-
-# isort - Import sorting (organized imports)
-isort==5.13.2
-
-# ============================================================================
-# YAML Processing (for governance/resource_auditor.py)
-# ============================================================================
-# PyYAML - YAML parsing (used in resource auditor for pipeline YAML)
-PyYAML==6.0.1
-
-# ============================================================================
-# Type Checking (Optional - for development)
-# ============================================================================
-# Mypy - Static type checking
-# mypy==1.7.1
-
-# ============================================================================
-# Documentation (Optional - for future Sphinx docs)
-# ============================================================================
-# Sphinx - Documentation generation
-# sphinx==7.2.6
-# sphinx-rtd-theme==2.0.0
-
-# ============================================================================
-# Version Compatibility Notes
-# ============================================================================
-# - All versions tested with Python 3.9
-# - Compatible with base_image="python:3.9" in KFP components
-# - google-cloud-aiplatform 1.56.0 supports KFP 2.7.0
-# - google-cloud-pipeline-components 2.14.1 requires KFP 2.7.0
-# - BigQuery libraries (google-cloud-bigquery, db-dtypes, pyarrow) work together
-# - Pytest versions compatible with Python 3.9
-#
-# Installation:
-#   pip install -r src/requirements.txt
-#
-# Update all packages (use with caution):
-#   pip install --upgrade -r src/requirements.txt