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Vertex AI & ML Platform | Google Cloud | Symhas
GCP · Vertex AI · Feature Store · MLOps Pipelines · Model Registry · Model Monitoring
Vertex AI Is the Most Complete ML Platform Available.
Most Teams Are Still Running Notebooks.Google Vertex AI provides Feature Store, managed training, Vertex AI Pipelines for MLOps automation, Model Registry with lineage, and Model Monitoring for drift detection — the full production ML stack, fully managed. The platform exists. The engineering to connect it correctly does not come pre-built.Symhas designs and deploys the full Vertex AI MLOps platform — Feature Store fed from BigQuery, Vertex AI Pipelines automating training and deployment, Model Registry with approval gates, and Model Monitoring triggering retraining when accuracy drifts — so data scientists deploy models in hours, not weeks.
Vertex AIEnd-to-end managed ML platform — Feature Store, Pipelines, Registry, Monitoring deployed
↓80%Model deployment time — Vertex AI Pipelines replacing manual notebook-to-production workflows
AutomatedModel retraining — drift threshold triggers Vertex AI Pipeline automatically
12wkVertex AI MLOps platform — Feature Store to Model Monitoring — production-ready, fixed price
GCP certified architects available nowActive
Vertex AIFull managed ML platform — Feature Store, Pipelines, Model Registry, and Model Monitoring
↓80%Model deployment time reduction — Vertex AI Pipelines replacing manual notebook workflows
AutomatedDrift-triggered model retraining — no manual intervention required
12wkVertex AI MLOps platform — Feature Store to production monitoring — fixed price
What We Deliver
Core Capabilities.
Production-Grade on GCP.

Every capability designed, deployed, and documented by Symhas GCP-certified data and AI architects. Fixed price. Production-ready.

Vertex AI Feature StoreOnline serving · Offline store · BigQuery · Feature groups · Point-in-time
Vertex AI Feature Store centralises ML features — online low-latency serving for real-time inference, offline BigQuery-backed storage for training, and point-in-time retrieval preventing training-serving skew by ensuring the same feature values are used in training and at inference time.
Feature group design — features organised by domain, entity type, and update frequency
Online store — low-latency feature retrieval for real-time model serving (p99 under 10ms)
Offline store — BigQuery-backed feature snapshots with point-in-time query for training
Feature pipeline — Cloud Dataflow or BigQuery jobs ingesting features into the online store
Feature reuse — features discoverable and shareable across data science teams and model projects
Training and inference use identical features — training-serving skew eliminated at the architecture level
Vertex AI Pipelines & MLOps AutomationKubeflow Pipelines · TFX · Cloud Build · CI/CD · Quality gates · Blue/green
Vertex AI Pipelines automate the end-to-end ML lifecycle using Kubeflow Pipelines or TFX — data validation, training, evaluation against quality gates, registration in the Model Registry, and deployment to Vertex AI Endpoints — triggered by code commit or scheduled retraining.
Pipeline definition — data validation, training, evaluation, and conditional deployment components
Model Registry — model versioning, metadata, lineage, and approval workflow
Quality gates — evaluation component blocks deployment if metric falls below agreed threshold
Cloud Build trigger — pipeline launched on training code commit to Cloud Source Repositories or GitHub
Blue/green endpoint deployment — traffic gradually shifted to new model version, rollback automated
Training commit to production endpoint automatically — quality gates blocking every underperforming model
Vertex AI Training & Custom ModelsCustom training · GPUs · Hyperparameter tuning · Distributed · Spot VMs
Vertex AI managed training for custom model development — GPU-accelerated training jobs, Vertex AI hyperparameter tuning finding optimal configurations, distributed training across multiple nodes for large models, and Spot VM training jobs reducing training cost by 60–80%.
Custom training jobs — any framework (TensorFlow, PyTorch, XGBoost, scikit-learn) on managed infrastructure
GPU training — A100, V100, T4 GPU types available for deep learning model training
Vertex AI Vizier — hyperparameter tuning across parameter space to optimise model performance
Distributed training — multi-node training for large models exceeding single-node memory
Spot VM training — preemptible training jobs reducing compute cost by 60–80%
Training infrastructure managed by GCP — data scientists write training code, not infrastructure configuration
Vertex AI Model Monitoring & Drift DetectionSkew detection · Drift · Prediction drift · Retraining trigger · Explainability
Vertex AI Model Monitoring continuously evaluates production model predictions for feature skew and prediction drift — alerting when input feature distributions deviate from the training baseline, and triggering automatic retraining pipelines when drift exceeds the configured threshold.
Training-serving skew detection — input feature distributions compared to training baseline
Prediction drift monitoring — output distribution shift detected and alerted
Custom metrics monitoring — business metric degradation detected alongside technical metrics
Retraining trigger — skew or drift beyond threshold automatically launches Vertex AI Pipeline
Vertex AI Explainability — SHAP feature attributions logged for every prediction at production endpoints
Model accuracy maintained automatically — drift triggers retraining before business impact is observed
Delivery Model
Assessment to Production.
Fixed Price. Fixed Timeline.

Four phases with go/no-go gates. Scope and price agreed before week one.

01
ML Platform Design & Workflow AssessmentWeeks 1–2

Data science workflow assessment. Vertex AI architecture — Feature Store schema, pipeline design, training infrastructure, and endpoint configuration. BigQuery feature source mapping. MLOps CI/CD design. Architecture approved.

02
Feature Store & Pipeline BuildWeeks 3–6

Vertex AI Feature Store groups created. Feature ingestion pipelines from BigQuery built. Vertex AI Pipeline skeleton with validation, training, evaluation, and deployment components. Model Registry configured with approval workflow.

03
Model Migration & MonitoringWeeks 7–10

Existing models migrated to Vertex AI training jobs. Production endpoints deployed and load-tested. Model Monitoring configured against training baseline. Drift threshold and retraining trigger tested with simulated skew event.

04
Go-Live & Data Science CertificationWeeks 11–12

Production traffic to Vertex AI endpoints. Pipeline CI/CD trigger live. Model Monitoring active. Data science team certified on Vertex AI Workbench and pipeline workflow. Symhas moves to advisory.

Retail · Vertex AI ML Platform500+ Location Retailer.
Demand Forecasting in Production. 33% Accuracy Gain. 12 Weeks.

The national retailer had data scientists building demand forecasting models in local Jupyter notebooks with no shared feature engineering, no model versioning, and no monitoring. Deploying a new model required manually copying files to a VM and updating a cron job.

Symhas built a Vertex AI MLOps platform — Feature Store with 120 engineered demand features from BigQuery, automated training pipelines with RMSE quality gates, Model Registry tracking 6 model versions in production across 4 product categories, and Model Monitoring detecting demand pattern shift during a supply chain disruption before the forecasts degraded.

↑33%Forecast accuracy
120Features in Vertex Feature Store
6Models in production via Registry
12wkTo production MLOps
Discuss Your Programme
What was delivered

Vertex AI MLOps Platform — Retail Production Deployment

→Feature Store — 120 demand features across 4 feature groups: sales history, promotions, seasonality, inventory
→Vertex AI Pipelines — automated training with data validation, RMSE gate at 0.18, and blue/green endpoint deployment
→Model Registry — 6 model versions across 4 product categories, lineage tracked from training data to endpoint
→Spot VM training — XGBoost training jobs on Spot VMs, 72% training cost reduction vs on-demand
→Model Monitoring — prediction drift detected during supply chain disruption in week 8, retraining triggered automatically
→Vertex AI Explainability — SHAP feature attributions logged for every forecast, enabling promotion impact attribution

“We went from cron jobs and manual file copies to a pipeline that deploys and monitors models automatically. Model Monitor caught a drift event before the business felt it in their inventory numbers. That is what mature ML operations looks like.”

— Head of Data Science, National Retailer

GCP Services Deployed
The Specific GCP Services
We Configure for This Capability.
GCP
Vertex AI Feature Store

Centralised feature repository — online serving, BigQuery offline store, point-in-time retrieval.

Feature group schema design
Online store for real-time serving
Offline BigQuery store
Feature ingestion pipeline
GCP
Vertex AI Pipelines

MLOps automation — Kubeflow or TFX pipelines, quality gates, Model Registry, CI/CD trigger.

Pipeline component design
Quality gate configuration
Model Registry integration
Cloud Build CI/CD trigger
GCP
Vertex AI Training

Managed training — custom containers, GPU jobs, hyperparameter tuning, distributed, Spot VMs.

Custom training job configuration
GPU type selection
Vertex AI Vizier tuning
Spot VM cost optimisation
GCP
Vertex AI Model Registry

Model versioning and lineage — metadata, approval workflow, and production deployment tracking.

Model version registration
Metadata and lineage
Approval workflow configuration
Deployment tracking
GCP
Vertex AI Endpoints

Managed inference — online serving endpoints with traffic splitting, autoscaling, and monitoring.

Endpoint configuration
Traffic splitting for blue/green
Autoscaling policy
Prediction logging
GCP
Vertex AI Model Monitoring

Production monitoring — skew detection, drift alerting, prediction drift, and retraining trigger.

Training-serving skew detection
Prediction drift monitoring
Drift threshold configuration
Retraining pipeline trigger
Why Symhas
GCP Expertise Built from Production Deployments.
Feature Store Before First Training RunModels trained without a Feature Store need retraining when one is introduced. Symhas deploys Feature Store and ingestion pipelines before the first model is trained on the platform — consistency from day one.
Pipelines With Quality Gates, Not Just AutomationA Vertex AI Pipeline that deploys regardless of model quality is not MLOps — it is deployment automation. Symhas configures evaluation quality gates that block deployment of underperforming models on every pipeline.
Drift-to-Retraining Loop Tested Before Go-LiveAutomatic retraining only matters if the full loop works. Symhas injects a simulated skew event and validates the full chain — drift detected, retraining triggered, new model deployed, monitoring baseline reset.
Spot VM Training Built In From the StartSpot VM training reduces GCP ML compute costs by 60–80%. Symhas configures Spot VM training jobs and checkpoint-based restart from the first training job — cost optimisation is architectural, not retrospective.
Explainability Active From First Production InferenceSHAP attributions are only useful if they are logged from go-live. Symhas activates Vertex AI Explainability on every production endpoint at launch — feature attribution available from inference one, not added when a stakeholder asks why.
Data Science Team on Vertex AI Workbench From Day OnePlatform adoption fails when workflows change entirely overnight. Symhas onboards data scientists to Vertex AI Workbench using the same notebook experience — MLOps wraps existing workflows before replacing them.
Also on GCP
Explore the Other GCP Capabilities.
📦GCP Data Platform Architecture

The BigQuery data warehouse and Dataflow pipelines that feed Vertex AI Feature Store.

✨Gemini & Generative AI

Vertex AI foundation models and Gemini — the generative AI layer built on this ML platform.

🔌Oracle ERP to GCP Integration

Oracle Fusion data flowing into BigQuery and Vertex AI Feature Store via GoldenGate CDC.

🔒GCP Security & Governance

VPC Service Controls protecting Vertex AI training data and model artefacts.

Next Step
Tell Us Where Your ML Models Are Today.
We Will Show You What Production ML on Vertex AI Looks Like.A 30-minute Vertex AI assessment with a Symhas ML architect. We will review your current model development workflow, feature engineering approach, and production deployment blockers — and design the MLOps platform that addresses them.
Book a GCP Assessment — 30 minView Client Outcomes
No commitment. No pitch deck. An honest conversation about your data and AI ambitions.