A Product Manager’s Guide to bringing out the "Good" in AI Models

As AI adoption accelerates, one question comes up repeatedly: What makes an AI model truly good? As a Product Manager advising teams on Google Cloud’s Vertex AI and other platforms, my role is to define, measure, and operationalize "goodness"—not just as model accuracy, but as a full-stack behavioral framework.

This guide is your step-by-step playbook to building responsible, helpful, and fair models on GCP. A step to move from MLOps to BehaviorOps

🌟 Step 1: Define "Goodness" in AI — Beyond Accuracy

A good model isn’t just accurate—it’s aligned with human values and safe in its outputs. Here’s a working definition of model behavior:

“A good model is helpful, honest, and fair, delivering accurate outputs while maintaining trust, transparency, and inclusivity.”

We break this down into three categories:

• Functional: Accuracy, latency, throughput.
• Behavioral: Helpfulness, honesty, fairness, adaptability.
• Operational: Reproducibility, transparency, safety.

🗺️ Step 2: Map the Metrics — Performance vs. Behavior

Let’s compare how traditional and modern model evaluations stack up:

The shift is clear: From evaluating raw performance to evaluating alignment with human needs.

♻️ Step 3: Implement the Lifecycle Using Vertex AI

To ensure these dimensions are built in—not bolted on—we follow a full-lifecycle MLOps setup using Vertex AI:

1. Data Processing: Use BigQuery + Dataflow for ETL pipelines. Integrate Data Validation.
2. Training Pipelines: Automate with Vertex Pipelines. Use Hyperparameter tuning.
3. Evaluation & Explainability: Integrate SHAP via Explainable AI APIs.
4. Bias & Fairness: Use TFX’s What-If Tool and Fairness Indicators for auditing.
5. Model Registry & Deployment: Register validated models, deploy via endpoints.
6. Continuous Monitoring: Track model drift and serving anomalies. Alert on data or concept drift.
7. Retraining Loops: Trigger Vertex Pipelines based on drift events.

🎯 Product Tip: Bake your model behavior guardrails into every phase—don’t wait till deployment.

🧠 Step 4: Product Guidance for Each Key Behavior

🏭 Real-World Example: AI in Manufacturing Quality Control

A global manufacturing company implemented a predictive quality model using Vertex AI to reduce defect rates on their production line. Initially, the model delivered over 93% accuracy but flagged high false positives for specific machine lines.

The problem? The model was trained on over-represented data from one shift and failed to generalize fairly across other lines.

The solution?

• Using Vertex AI’s Fairness Indicators, the team uncovered demographic skew by shift and location.
• Implemented SHAP-based explainability to communicate why the model flagged certain items.
• Incorporated continuous monitoring to identify drift across batches.

The result? Reduced false positives by 41%, improved operator trust, and avoided unnecessary downtime worth $200K/month.

This wasn’t just about model performance—it was about model behavior that earns trust at scale.

🚀 Final Word: From MLOps to BehaviorOps

We’ve evolved from building models to building behavioral systems.

As a Product Manager guiding AI initiatives, your job isn’t just to ship models—it’s to ship models that behave well.

With GCP Vertex AI, we can encode our product philosophy into real-world systems:

• Structure for reproducibility
• Metrics for human alignment
• Guardrails for safety and fairness

Let’s redefine what it means to say an AI model is “good.”

⚙️Open-Source Options for Lean Teams

Not every org needs full Vertex AI. You can build similar pipelines using open-source components:

Let’s Discuss 👇 What’s one trait you think every responsible AI model must have? Tag your favorite AI PMs and let’s debate what “good AI” truly means.