8.3 Modeling Techniques for Interpretability Requirements

Google Cloud Professional Machine Learning Engineer PMLE

Track your progress as you read

Finished sections get a check mark.

1 Chapter 1: Exam Overview & the Agent Platform Map

1.1 PMLE Exam Facts, Policies & the June 2026 Blueprint 1.2 From Vertex AI to Gemini Enterprise Agent Platform: The Product Name Map

2 Chapter 2: Low-Code ML with BigQuery ML

2.1 Choosing, Training & Predicting with BigQuery ML Models 2.2 Feature Engineering & Feature Selection in BigQuery ML 2.3 Fine-Tuning & Evaluating Gemini Models from BigQuery

3 Chapter 3: AutoML & Google Cloud AI APIs

3.1 Training Models with Agent Platform AutoML 3.2 Building with Document AI, Vision, Translation & Other AI APIs

4 Chapter 4: Foundation Models in Model Garden

4.1 Evaluating & Selecting Models in Model Garden 4.2 Building & Tuning Solutions with Gemini, Imagen, Veo & MaaS 4.3 Optimizing Gemini Applications for Cost, Latency & Availability

5 Chapter 5: Exploring, Preprocessing & Protecting Data

5.1 Organizing & Exploring Tabular, Text & Image Data 5.2 Choosing a Preprocessing Tool: BigQuery, Dataflow, Spark or Python 5.3 Creating & Consolidating Features in Agent Platform Feature Store 5.4 Protecting PII & Sensitive Data in ML Workflows

6 Chapter 6: Notebooks, Prototyping & Experiment Tracking

6.1 Workbench vs. Colab Enterprise: Secure, Collaborative Notebooks 6.2 Prototyping with PyTorch, scikit-learn, JAX & Model Garden in Notebooks 6.3 Experiment Environments, Tracking & Lineage with Experiments and ML Metadata

7 Chapter 7: Evaluating Predictive & Generative AI Solutions

7.1 Evaluation Metrics for Predictive Models 7.2 Evaluating Gen AI: Computation Metrics, Rubrics & LLM-as-a-Judge

8 Chapter 8: Choosing the Modeling Approach

8.1 Choosing the Model Type: ARIMA, Trees, DNNs & LLMs 8.2 Choosing the Product & Deployment Strategy 8.3 Modeling Techniques for Interpretability Requirements

9 Chapter 9: Training Data & Training SDKs

9.1 Organizing & Ingesting Training Data from Cloud Storage & BigQuery 9.2 Custom Training on Agent Platform: Containers, Jobs & Worker Pools 9.3 Kubeflow on GKE, AutoML & Tabular Workflows as Training Choices

10 Chapter 10: Troubleshooting, Hyperparameter Tuning & Fine-Tuning

10.1 Troubleshooting ML Training Failures 10.2 Hyperparameter Tuning on Agent Platform 10.3 Fine-Tuning Foundation Models & When to Tune

11 Chapter 11: Training Hardware & Distributed Training

11.1 Choosing CPUs, GPUs & TPUs for Training 11.2 Distributed Training: Data & Model Parallelism

12 Chapter 12: Deploying Models for Inference

12.1 Batch vs. Online Inference on Agent Platform 12.2 Serving Beyond Agent Platform Endpoints: Model Garden, Cloud Run & GKE 12.3 Prebuilt & Custom Serving Containers

13 Chapter 13: Model Versions, Rollouts & Inference Logic

13.1 Organizing & Versioning Models in Model Registry 13.2 Rollout Strategies: A/B Tests, Canaries & Traffic Splits 13.3 Inference Preprocessing & Postprocessing

14 Chapter 14: Scaling Online Serving

14.1 Serving Features Online with Feature Store 14.2 Public, Dedicated & Private Endpoints 14.3 Serving Hardware & Autoscaling for Throughput 14.4 Tuning Models for Production Training & Serving

15 Chapter 15: End-to-End ML Pipelines

15.1 Validating Data & Models in Pipelines 15.2 Agent Platform Pipelines with KFP: Components, Templates & Caching 15.3 Ensuring Consistent Preprocessing Between Training & Serving

16 Chapter 16: Orchestration with Managed Airflow & Ray

16.1 Orchestrating ML Workflows with Managed Service for Apache Airflow 16.2 Ray on Agent Platform for Distributed ML Workloads

17 Chapter 17: Automating Retraining & CI/CD/CT

17.1 Choosing a Retraining Policy 17.2 CI/CD/CT for ML with Cloud Build

18 Chapter 18: Securing AI Systems, Responsible AI & Explainability

18.1 Protecting AI Systems from Data & Model Exfiltration 18.2 Defending Gen AI Apps: Safety Filters, Model Armor & Regex Guardrails 18.3 Responsible AI: Detecting & Monitoring Bias 18.4 Model Explainability on Agent Platform

19 Chapter 19: Monitoring AI Solutions

19.1 Configuring Model Monitoring on Agent Platform 19.2 Training-Serving Skew, Data Drift, Concept Drift & Attribution Drift 19.3 Monitoring, Testing & Evaluating Gen AI Solutions in Production

8.3 Modeling Techniques for Interpretability Requirements

Key Takeaways Explain This Topic AI

Intrinsically interpretable models such as linear and logistic regression expose weights directly, while tree ensembles and DNNs need post-hoc explanation methods.

Agent Platform feature attribution methods are sampled Shapley for non-differentiable models, integrated gradients for differentiable models, and XRAI for image regions.

BigQuery ML explains boosted tree and random forest models with Tree SHAP or approximate feature contribution, and DNNs with integrated gradients.

Feature attributions are measured relative to a baseline, so the choice of baseline changes how attributions should be read.

Vertex Explainable AI was deprecated on March 16, 2026, and Google points to open-source SHAP and LIME as alternatives.

Last updated: September 2026

The exam guide lists modeling techniques given interpretability requirements as part of building models. Interpretability can be a legal requirement (explaining a credit denial), a trust requirement (clinicians must see why), or a debugging aid. This section is about choosing the technique. Implementing and monitoring explanations in production is covered in Section 18.4.

Two Paths to Interpretability

Path Examples Pros Cons

Intrinsically interpretable ("glass-box") models Linear or logistic regression, shallow decision trees, rule lists, ARIMA_PLUS decomposition Explanations are exact and stable. Easy to audit Can lose accuracy on complex non-linear data

Post-hoc explanations of complex models Shapley-based feature attributions for tree ensembles and DNNs, saliency for images, example-based explanations Keep high accuracy Approximations that depend on method and baseline. Harder to validate

Decision rule: if a regulator or policy requires a precise, stable reason for each decision and the accuracy gap is small, prefer a glass-box model . If complex models are much more accurate and approximate explanations are acceptable, use a complex model plus post-hoc attributions , and validate that the explanations make sense.

Local vs. Global Explanations

Local explanations show how each feature contributed to one prediction ("income and debt ratio lowered this applicant's score"). They're needed for individual adverse-action reasons.

Global explanations show a feature's overall influence across the dataset. They're used for model validation and governance reviews.

Explanation Methods on Google Cloud
Agent Platform feature attributions

All three methods are based on Shapley values , which credit each feature for its share of the outcome.

Method Best for Notes

Sampled Shapley Non-differentiable models such as tree ensembles, and meta-ensembles of trees and neural networks (AutoML tabular) Sampling approximation of exact Shapley values. Works with any custom-trained model in any container

Integrated gradients Differentiable models (neural networks), especially with large feature spaces. Low-contrast images such as X-rays Integrates gradients along a path from a baseline to the input

XRAI Image classification with natural images Builds on integrated gradients, then oversegments the image and ranks regions by attribution

Example-based explanations return the most similar training examples, found by nearest-neighbor search on embeddings. They help debug mislabeled or underrepresented data and flag inputs far from anything seen in training. They support TensorFlow models that produce embeddings, not tree models.

BigQuery ML explanations

Model Method Functions

Linear and logistic regression Shapley values (weight × standardized feature), plus standard errors and p-values ML.EXPLAIN_PREDICT , ML.GLOBAL_EXPLAIN , ML.ADVANCED_WEIGHTS

Boosted trees, random forest Tree SHAP (exact for trees), approximate feature contribution, Gini-based importance ML.EXPLAIN_PREDICT , ML.GLOBAL_EXPLAIN , ML.FEATURE_IMPORTANCE

DNN, Wide & Deep Integrated gradients ML.EXPLAIN_PREDICT , ML.GLOBAL_EXPLAIN
AutoML Tables Sampled Shapley ML.GLOBAL_EXPLAIN
ARIMA_PLUS Time series decomposition ML.EXPLAIN_FORECAST
Baselines matter

Attributions measure contribution relative to a baseline , such as the median input for tabular data or an all-black image. Change the baseline and the attributions change. Pick a baseline that fits the question, like "compared with a typical applicant."

Lifecycle Note: Explainable AI Deprecation

Vertex Explainable AI was deprecated on March 16, 2026 and is scheduled to shut down on March 16, 2027 . No new features are being added, and Google recommends open-source libraries such as SHAP and LIME as alternatives. For long-lived designs:

Prefer glass-box models where they meet accuracy needs.

Use BigQuery ML explanation functions for BigQuery ML models.

Package SHAP (Shapley-based, including TreeSHAP for trees) or LIME (local surrogate models) in training, batch, or serving code for custom models.

Techniques That Improve Interpretability Without Losing Much Accuracy

Feature selection: fewer, meaningful features make any explanation clearer (Chapter 2).

Domain-meaningful feature engineering: "debt-to-income ratio" explains better than 40 raw balance columns.

Constraints: limit tree depth, or use monotonic relationships where the domain requires them ("higher income never lowers the score").

Two-stage designs: a glass-box model for the regulated decision, and a complex model only for non-regulated ranking.

Model documentation: record intended use, training data, evaluation slices, and limitations.

Matching Requirements to Techniques

Requirement Suitable approach
Exact per-decision reason codes for a regulator Linear or logistic regression, or shallow trees

Global validation that the model uses sensible drivers Global attributions ( ML.GLOBAL_EXPLAIN , SHAP summaries) on any model

Explain which image region drove a diagnosis Integrated gradients or XRAI saliency on the vision model

Explain an unexpected prediction by showing similar past cases Example-based explanations or nearest-neighbor retrieval on embeddings

Explain a forecast to planners ARIMA_PLUS decomposition with ML.EXPLAIN_FORECAST
Interpretability for Generative AI

LLMs don't provide faithful feature attributions, and a model's self-written "reasoning" isn't a guaranteed account of how it produced an answer. For gen AI, interpretability usually means:

Grounding with citations , so users can check sources (Chapter 4).

Structured outputs that separate the decision from the supporting evidence.

Evaluation of grounding and instruction following (Chapter 7).

Keeping regulated decisions in interpretable models, with the LLM summarizing or drafting only.

Worked Scenario

A lender must give each rejected applicant the top reasons for denial, and model-risk reviewers must approve the model yearly.

A logistic regression with well-engineered features reaches AUC 0.81. A boosted tree reaches 0.83.

Adverse-action reasons must be exact and consistent, and the 0.02 AUC gain doesn't offset the review burden. The team picks logistic regression , with reason codes from each feature's contribution (weight × value).

A separate boosted tree with Tree SHAP explanations ranks marketing offers, where approximate explanations are acceptable.