IMPACT VERTEX AI MLOps FRAMEWORK v1.0

ACCELERATE END TO END MLOps ON VERTEX AI WITH VERTEXAI.IMPACT 

Modern AI workflows are breaking the seams of traditional DevOps. While most MLOps frameworks either overfit to academic rigor or collapse under organizational friction, the IMPACT Vertex AI MLOps frameworkis engineered as a pragmatic execution system: scalable, composable, and deeply integrated with Google Cloud’s Vertex AI ecosystem. It exists to answer a clear need: a repeatable, production-ready framework that empowers product managers, data scientists, ML engineers, and platform leads to build intelligent systems on GCP Vertex AI that not only ship — but evolve.

Purpose, Scope, & Value

The IMPACT Vertex AI MLOps Framework is a pragmatic, production-grade MLOps execution system purpose-built for teams operating within the Google Cloud Vertex AI ecosystem. From data ingestion to automated deployment, it provides a structured path for turning ML experimentation into scalable, continuously evolving production systems.

Designed to bridge the gap between ML theory and enterprise AI delivery, this framework helps PMs, ML engineers, and platform teams align around a shared pipeline — one that supports telemetry, feedback loops, and model adaptation by default. It brings orchestration, experimentation, and observability under a single native stack, enabling tighter iteration cycles and cleaner handoffs across functions.

It interoperates cleanly with sibling frameworks like IMPACT AI Product Management, IMPACT Tech Product Management, and IMPACT Technical Project Management frameworks— forming a cohesive execution layer for AI-native and platform-aligned organizations building at scale.

Why it stands apart:

• Closes the gap between prototype and production by operationalizing the full Vertex AI toolchain
• Removes orchestration friction by aligning to native GCP workflows end-to-end
• Embeds continuous training, prototyping, and tuning into every stage — not just the final mile
• Supports drift-resilient systems with built-in hooks for retraining and feature evolution
• Brings product, engineering, and infra into sync through shared metrics, artifacts, and model lifecycle discipline

Guiding Principles

• Aligned with IMPACT AI PM Framework: IMPACT Vertex AI MLOps framework inherits the system discipline, rhythm, and stage structure of IMPACT AI PM framework to ensure end-to-end continuity.
• Modular and Composable: Each stage can stand alone or integrate into an org’s existing ML tooling — enabling gradual adoption.
• Vertex-Native First: Designed to work with Vertex AI out of the box — from BigQuery to Pipelines to Endpoints — for minimal overhead.
• Built to Scale: Supports progression from prototype to production without structural redesign — empowering both lean startups and scaled AI infra teams.

Who Is This Framework For 

• ML Ops Leaders & Engineers who need repeatable, auditable, and high-performance deployment systems across teams and models.
• AI/ML Engineers looking for fast, reliable ways to move models from notebook to endpoint using Vertex-native pipelines.
• Data Engineers building and managing upstream pipelines, feature stores, and ingestion frameworks that power ML workloads.
• Data Scientists needing an ecosystem to experiment, fine-tune, and validate models without falling into dev friction.
• DevOps Engineers tasked with owning uptime, deployment governance, and post-launch observability in complex ML stacks.

A 5-stage execution framework for modern AI/ML leaders building transformational systems on top of GCP Vertex AI.

Stage 1: Data and Feature Factory

 Goal:

Establish a robust, production-grade data ingestion pipeline and feature store to power downstream ML modeling and personalization workflows.

Inputs:

• Raw structured/unstructured data (event logs, user data, etc.)
• External and internal data sources
• Metadata schemas
• Data governance rules
  

Outputs:

• Unified data ingestion pipeline
• Feature engineering workflows
• Versioned feature catalog in Vertex AI Feature Store
  

Artifacts:

• Feature Definition Matrix
• Data Ingestion DAG
• Feature Store Snapshot
• Data Quality Scorecard
  

Steps:

1. Set up scalable ingestion from data lakes or BigQuery to Vertex AI datasets
2. Engineer features using Spark, dbt, or SQL pipelines and define feature schema
3. Store engineered features with version control in Vertex AI Feature Store
4. Validate data quality and perform automated checks for feature drift
5. Document feature ownership, transformation logic, and reusability across models
   

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Stage 2: Model Factory

Goal:

Train, select, and version the best-fit ML or LLM model aligned to use case objectives using native Vertex AI tooling.

Inputs:

• Prepared datasets and feature sets
• Model training configuration
• Vertex AI Experiments and hyperparameters
• Evaluation metrics (accuracy, precision, latency)
  

Outputs:

• Trained model artifacts
• Model comparison report
• Performance benchmarking matrix
• Registered model in Artifact Registry
  

Artifacts:

• Experiment Tracking Dashboard
• Model Card (Performance, Bias, Latency)
• Artifact Registry Entry
• Hyperparameter Tuning Log
  

Steps:

1. Launch training via Vertex AI Training or AutoML with managed resources
2. Track experiment results, evaluation metrics, and model metadat
3. Benchmark models across scenarios using standardized evaluation pipelines
4. Register best-performing model in the Artifact Registry with version ID
5. Document lineage, tuning decisions, and acceptance criteria
   

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Stage 3: Prototype Deployment

Goal:

Deploy the trained model in a staging environment to validate assumptions, simulate real-world conditions, and collect early feedback.

Inputs:

• Registered model artifact
• Staging environment configuration
• Baseline acceptance thresholds
• Simulated or shadow production data
  

Outputs:

• Prototype deployment snapshot
• Real-world inference logs
• Feedback-informed model adjustment plan
  

Artifacts:

• Deployment Playbook (Staging)
• Observability Setup (Latency, Drift, Accuracy)
• Inference Evaluation Log
• Adjustment Recommendations Brief
  

Steps:

1. Deploy model to Vertex AI Endpoints (staging) with logging enabled
2. Simulate live traffic or replay historical queries to test model responses
3. Monitor performance, cost, latency, and prediction relevance
4. Analyze drift, edge cases, and error boundaries
5. Develop action plan for model tuning prior to production rollout
   

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Stage 4: Production Deployment

Goal:

Launch the model into production with hardened performance, full observability, and automated incident management across the lifecycle.

Inputs:

• Validated model artifact
• SLA thresholds
• Monitoring and alert configurations
• Infrastructure as Code (IaC) setup
  

Outputs:

• Production-grade deployment configuration
• Live monitoring dashboard
• Alert triggers and escalation paths
• Versioned deployment logs
  

Artifacts:

• Production Deployment Blueprint
• Service Level Objectives (SLOs) Document
• Drift Detection Configuration
• Alert Routing Plan
  

Steps:

1. Deploy model to Vertex AI Endpoints (production) with autoscaling
2. Set up live monitoring with Cloud Monitoring and Vertex AI logs
3. Enable latency, usage, and error tracking
4. Integrate alerting with Slack, PagerDuty, or equivalent systems
5. Maintain rollback logic and versioned endpoint routing
   

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Stage 5: Automated Pipelines (CI/CD)

Goal:

Enable continuous integration, retraining, and delivery pipelines with built-in feedback and change triggers to maintain long-term model performance.

Inputs:

• Production logs and model performance metrics
• Updated training data
• Feature evolution plans
• CI/CD triggers and schedules
  

Outputs:

• Fully automated training and deployment pipeline
• Continuous training logs and history
• Updated feature sets and retraining records
• Post-deployment validation reports
  

Artifacts:

• CI/CD Pipeline YAML (Cloud Build/Deploy)
• Retraining Trigger Definitions
• Re-Prompting and Re-Routing Logic
• Feature Enhancement Summary
  

Steps:

1. Configure CI/CD pipeline using Cloud Build, Vertex AI Pipelines, and Artifact Registry
2. Trigger retraining jobs based on drift thresholds, KPI degradation, or new data
3. Re-evaluate models against updated benchmarks
4. Push retrained models into staging and repeat prototype validation
5. Automatically update production models via canary rollout or approval gating
   

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Stage 7: Data and Feature Factory

Goal: Establish a robust data pipeline and feature store to support all downstream ML tasks.

Components:

• Google Cloud Storage
• BigQuery
• Bigtable
• Vertex AI Datasets
• Vertex AI Feature Store

Outputs:

• Data Ingestion Pipeline
• Feature Engineering Workflow
• Feature Catalog

Stage 8: Model Factory

Goal: Select, train, and version the best-fit model or LLM for the target use case.

Components:

• Vertex AI Model Garden
• Vertex AI Experiments
• Vertex AI Training (standard + custom containers)
• AutoML
• Foundation Model Fine-Tuning
• API-based model integrations (e.g., entity extraction, voice)
• Jupyter Notebooks
• Vertex AI Workbench
• Artifact Registry

Outputs:

• Trained Model Artifact
• Model Comparison Report
• Benchmarking Matrix
• Artifact Registry Record

Stage 9: Prototype Deployment

Goal: Deploy the model in a controlled environment to validate assumptions and gather real feedback.

Components:

• Vertex AI Pipelines
• Vertex AI Endpoints (test/staging)
• Jupyter Notebooks
• Logging + Observability Tools

Outputs:

• Prototype Deployment Snapshot
• Early Feedback Report
• Model Adjustment Plan

Stage 4: Production Deployment

Goal: Launch a production-grade model with full observability, performance monitoring, and reliability.

Components:

• Vertex AI Endpoints (production)
• Vertex AI Pipelines
• Deployment Manager (Infrastructure as Code)
• Monitoring & Logging (Cloud Monitoring, Cloud Trace)

Outputs:

• Production Deployment Blueprint
• Monitoring Dashboard
• Alert System Config

Stage 10: Automated Pipelines (CI/CD)

Goal: Enable continuous integration, deployment, and improvement across models and features.

Sub-Phases:

• Human-in-the-Loop Deployment
• Continuous Training
• Feature Enhancements

Components:

• Cloud Build
• Cloud Deploy
• Deployment Manager
• Artifact Registry
• Vertex AI Pipelines

Outputs:

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CI/CD Pipeline Configuration

Continuous Training Logs

Feature Enhancement Reports

Deployment Logs