Lesson 6: BigQuery ML & TensorFlow on GCP

1. BigQuery ML (BQML)

BigQuery ML lets data analysts train and serve ML models using SQL within BigQuery — no need to export data or learn ML frameworks.

BigQuery ML Workflow:

1. CREATE MODEL → train
2. ML.EVALUATE() → evaluate metrics
3. ML.PREDICT() → generate predictions
4. ML.EXPLAIN_PREDICT() → SHAP-based explanations
5. EXPORT MODEL → export to Cloud Storage (TF SavedModel format)

Model Type	BQML Option	Task
Linear Regression	LINEAR_REG	Regression
Logistic Regression	LOGISTIC_REG	Binary/Multiclass classification
K-Means	KMEANS	Clustering
XGBoost	BOOSTED_TREE_CLASSIFIER / BOOSTED_TREE_REGRESSOR	Tabular classification/regression
Random Forest	RANDOM_FOREST_CLASSIFIER / RANDOM_FOREST_REGRESSOR	Tabular classification/regression
DNN	DNN_CLASSIFIER / DNN_REGRESSOR	Complex patterns
Wide & Deep	WIDE_AND_DEEP_CLASSIFIER	Recommendations (memorization + generalization)
AutoML	AUTOML_CLASSIFIER / AUTOML_REGRESSOR	Automated model selection
Time Series	ARIMA_PLUS	Forecasting
Matrix Factorization	MATRIX_FACTORIZATION	Collaborative filtering

Exam tip: BQML ARIMA_PLUS automatically handles seasonality, holiday effects, and trend decomposition. When the question asks "forecast using BigQuery data" → ARIMA_PLUS. When it asks "recommendation system in BigQuery" → MATRIX_FACTORIZATION.

2. TensorFlow Extended (TFX)

TFX is a production ML pipeline library for TensorFlow. It provides standard components for each step in the ML lifecycle.

TFX Component	Purpose
ExampleGen	Ingest data from CSV, BigQuery, Avro, Parquet
StatisticsGen	Compute statistics on training data
SchemaGen	Infer schema from statistics
ExampleValidator	Detect anomalies: missing data, distribution skew
Transform	Feature engineering (Apache Beam-based)
Trainer	Train TF model (EvalSpec + TrainSpec)
Tuner	Hyperparameter tuning (KerasTuner)
Evaluator	Evaluate model against baseline
ModelValidator	Validate model meets quality thresholds
Pusher	Push model to serving (TF Serving, Vertex AI)

TFX Pipeline (simplified):

ExampleGen → StatisticsGen → SchemaGen → ExampleValidator
                ↓
            Transform (feature engineering)
                ↓
            Trainer (model training)
                ↓
            Evaluator (metrics vs baseline)
                ↓ (if pass)
            Pusher → TF Serving / Vertex AI Endpoint

3. TF Serving & TFLite

Option	Use Case
TF Serving	High-performance serving on server/cloud (gRPC or REST)
TFLite	Mobile devices, edge devices, microcontrollers
TF.js	Browser-based inference

4. Model Optimization Techniques

Technique	Description	Trade-off
Quantization	Float32 → INT8 weights	4x smaller, ~2x faster, slight accuracy loss
Pruning	Remove low-weight connections	Smaller model, preserve accuracy
Knowledge Distillation	Train small "student" model from large "teacher"	Smaller + fast, slight accuracy loss
TensorRT	NVIDIA GPU optimization (layer fusion)	3-5x inference speedup on NVIDIA GPUs

5. Practice Questions

Q1: A data analyst team needs to build a sales forecasting model on data already in BigQuery. They are comfortable with SQL but have no Python/ML framework experience. Which BigQuery ML model type should they use for time series forecasting?

• A) KMEANS
• B) LOGISTIC_REG
• C) ARIMA_PLUS ✓
• D) MATRIX_FACTORIZATION

Explanation: BigQuery ML ARIMA_PLUS is designed for time series forecasting and automatically handles seasonality, trend, and holiday effects. It can be trained with a simple CREATE MODEL statement in SQL, requiring no Python expertise.

Q2: A TFX pipeline is detecting that the distribution of the "age" feature in new production data differs significantly from the training data distribution. Which TFX component is responsible for detecting this anomaly?

• A) StatisticsGen
• B) SchemaGen
• C) ExampleValidator ✓
• D) Transform

Explanation: ExampleValidator compares data statistics against the expected schema and flags anomalies including distribution skew (significant difference between training and serving data distributions). StatisticsGen computes statistics; SchemaGen creates the schema; Transform does feature engineering.

Q3: A team needs to deploy a TensorFlow image classification model to mobile devices with limited compute resources. They need to reduce model size by 4x with minimal accuracy loss. Which technique should they apply?

• A) Knowledge Distillation
• B) Model Pruning
• C) Post-training quantization (INT8) ✓
• D) TensorRT optimization

Explanation: Post-training quantization converts Float32 weights to INT8, reducing model size by approximately 4x and improving inference speed by 2x, with minimal accuracy loss for most models. TFLite supports INT8 quantization for mobile/edge deployment. TensorRT is for NVIDIA GPUs, not mobile.