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AWS · SageMaker · Bedrock · Feature Store · MLOps · Model Monitor · Production ML
Your ML Models Work in the Notebook.
Symhas Gets Them Working in Production.The gap between a working Jupyter notebook and a reliable production ML model is an engineering gap. Feature pipelines, model registries, deployment automation, drift monitoring, and retraining triggers — infrastructure problems most data science teams have not solved. Symhas builds the platform that makes production ML reliable.Symhas designs and deploys Amazon SageMaker Feature Store, MLOps Pipelines, Model Registry, and Model Monitor — plus Amazon Bedrock for LLM integration on private enterprise data — so data science focuses on models and production models stay accurate over time.
SageMakerAmazon SageMaker — Feature Store, Pipelines, Model Registry, and Model Monitor deployed
↓70%ML infrastructure management time — SageMaker MLOps replacing manual notebook-to-prod workflows
AutomatedModel retraining — drift threshold triggers the SageMaker Pipeline without human intervention
12wkSageMaker MLOps platform or Bedrock integration — production deployment, fixed price
AWS certified architects available nowActive
↓70%ML infrastructure management time after SageMaker MLOps platform deployment
AutomatedModel retraining — drift detection triggers full pipeline without human intervention
ProductionEvery Symhas ML engagement ends with models serving real traffic — not in staging
12wkSageMaker MLOps or Bedrock integration — production deployment, fixed price
What We Deliver
Core Capabilities.
Production-Grade on AWS.

Every capability designed, deployed, and documented by Symhas AWS-certified architects. Fixed price. SLA-backed from go-live.

SageMaker MLOps PipelinesPipelines · Model Registry · Quality gates · CI/CD · Blue/green deployment
SageMaker MLOps Pipelines automate the full model lifecycle — data preprocessing, training, evaluation against quality gates, registration in the Model Registry, and deployment — triggered by code commit or scheduled retraining.
Pipeline definition — preprocessing, training, evaluation, and conditional deployment steps
Model Registry — model versioning, metadata, and business owner approval workflow
Quality gates — evaluation step blocks deployment below the agreed accuracy threshold
CI/CD trigger — CodePipeline or GitHub Actions triggers the SageMaker Pipeline on commit
Deployment strategies — blue/green and canary for zero-downtime endpoint updates
Training commit to production endpoint automatically — quality gates blocking every bad model
SageMaker Feature StoreOnline store · Offline store · Point-in-time queries · Feature groups · Reuse
SageMaker Feature Store centralises ML features — real-time online store for inference, offline store for training, and point-in-time queries preventing training-serving skew by ensuring training and inference use identical feature values.
Feature group schema design — organised by domain and update frequency
Online store — millisecond feature retrieval for real-time inference endpoints
Offline store — S3-backed Parquet with point-in-time query for training consistency
Feature pipeline — Kinesis streaming or batch ingestion keeping stores synchronised
Feature discovery — searchable and reusable across teams and projects
Training-serving skew eliminated at the architecture level — same features, same values
Amazon Bedrock & LLM IntegrationClaude · Llama · Knowledge Base · RAG · Guardrails · VPC endpoint
Amazon Bedrock gives access to Anthropic Claude, Meta Llama, Mistral, and other models via a single API — with Knowledge Bases for RAG on private data, Guardrails for PII redaction and topic blocking, and a VPC endpoint so data never traverses the public internet.
Model selection — Claude, Llama, or Mistral based on use case and compliance requirements
Bedrock Knowledge Base — RAG on S3 documents with OpenSearch Serverless vector store
Bedrock Guardrails — topic blocking, content filtering, PII redaction
Bedrock Agents — multi-step LLM workflows with tool use and action groups
VPC endpoint — Bedrock accessed privately, no data to the public internet
LLM on private enterprise data — compliant, auditable, not traversing the internet
Model Monitor & Drift DetectionData quality · Model quality · Bias · Drift threshold · Retraining trigger
SageMaker Model Monitor evaluates data quality, model quality, and feature attribution drift continuously — triggering the retraining pipeline automatically when degradation reaches the defined threshold.
Data quality monitoring — feature distributions vs training baseline, drift alerts
Model quality monitoring — accuracy vs labelled ground truth continuously evaluated
Bias monitoring — SageMaker Clarify detecting protected attribute bias in live predictions
Drift threshold — configurable per model, triggers retraining pipeline automatically
CloudWatch dashboard — accuracy, endpoint latency, and drift scores in one view
Model accuracy maintained automatically — retraining triggered before business stakeholders notice
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 & Use Case AssessmentWeeks 1–2

Data science workflow assessment. SageMaker architecture — Feature Store schema, MLOps pipeline, inference endpoint design. Bedrock use case scoping. IAM and VPC configuration. Architecture approved.

02
SageMaker Platform BuildWeeks 3–6

SageMaker Studio configured. Feature Store groups created, ingestion pipelines built. MLOps Pipeline with preprocessing, training, evaluation, and deployment steps. Model Registry with approval workflow.

03
Model Migration & Bedrock IntegrationWeeks 7–10

Existing models migrated to SageMaker training jobs. Inference endpoints deployed and load-tested. Bedrock Knowledge Base built and RAG pipeline tested. Model Monitor configured against baseline.

04
Go-Live & Data Science CertificationWeeks 11–12

Production traffic to SageMaker endpoints. Drift monitoring active. Retraining pipeline tested with simulated drift event. Data science team certified on SageMaker Studio and MLOps workflow. Symhas moves to advisory.

Financial Services · AWS AI/ML$25B AUM Asset Manager.
3x Faster Model Deployment. 8% Degradation Caught Before Business Impact.

Data scientists were building credit risk and portfolio models in Jupyter notebooks on local laptops. Getting a model to production required 3 weeks of manual DevOps coordination. Model performance was never monitored after deployment.

Symhas built a SageMaker MLOps platform — Feature Store for risk and market data, automated pipelines with quality gates, Model Registry with approval workflow, and Model Monitor catching 8% accuracy degradation in a production credit risk model before any business impact.

3xFaster model deployment
8%Degradation detected by Monitor
AutomatedRetraining on drift
12wkTo production platform
Discuss Your Programme →
What was delivered

Amazon SageMaker MLOps — Financial Services Deployment

→SageMaker Studio — 12 data scientists with individual profiles and shared project spaces
→Feature Store — 340 features across 6 groups: market, counterparty, portfolio, macro, credit, liquidity
→MLOps Pipeline — training, AUC gate at 0.82, Model Registry, endpoint deployment automated
→Model Registry — 8 versions registered in first month, unapproved models blocked from production
→Model Monitor — 8% accuracy degradation detected in week 6, retraining triggered automatically
→Amazon Bedrock — Claude 3 Sonnet via RAG on internal research documents for portfolio Q&A

“We went from 3 weeks to deploy a model to 2 hours. The pipeline does what used to take two people a week. And Model Monitor caught a degradation we would have found from a business complaint months later.”

— Chief Data Officer, Global Asset Management Firm

Faster model deployment3x
Degradation detected by Monitor8%
Retraining on driftAutomated
To production platform12wk
AWS Services Deployed
The Specific AWS Services
We Configure for This Capability.
AWS
Amazon SageMaker

End-to-end ML platform — Studio, training jobs, processing jobs, real-time inference endpoints.

SageMaker Studio configuration
Training job and Spot optimisation
Processing job design
Real-time and batch endpoints
AWS
SageMaker Feature Store

Feature repository — online and offline store, point-in-time queries, cross-team discovery.

Feature group schema design
Online store for inference
Offline store with point-in-time queries
Batch and streaming ingestion
AWS
SageMaker Pipelines & Model Registry

MLOps automation — pipeline definition, quality gates, model versioning, approval, deployment.

Pipeline step definition
Quality gate configuration
Model Registry and approval workflow
Blue/green endpoint deployment
AWS
SageMaker Model Monitor

Production monitoring — data quality, model quality, bias detection, retraining trigger.

Data quality baseline and monitoring
Model quality monitoring
Bias detection via Clarify
Drift threshold and retraining trigger
AWS
Amazon Bedrock

Managed LLM API — Claude, Llama, Mistral with Knowledge Bases and Guardrails.

Model access configuration
Knowledge Base and OpenSearch vector store
Guardrails for PII and topics
Bedrock Agents
AWS
Amazon SageMaker Clarify

Explainability and bias — SHAP values, feature importance, bias monitoring for compliance.

SHAP explainability
Feature importance analysis
Bias metric monitoring
Explainability reports
Why Symhas
AWS Expertise Built from Production Deployments.
01
Full MLOps Stack, Not a Notebook ServerSageMaker Studio without Feature Store, Pipelines, and Model Monitor is just a managed notebook. Symhas deploys the full MLOps stack — every component that makes production ML reliable.
02
Feature Store Before First Model TrainingModels trained without Feature Store need retraining when one is added. Symhas deploys Feature Store before the first model is trained on the platform — consistency from day one.
03
Bedrock via VPC Endpoint OnlyLLM in regulated industries requires data to stay in the private network. Symhas deploys Bedrock via VPC endpoint with Guardrails for PII redaction — compliance-ready from day one.
04
Model Monitor Baseline Before Production TrafficMonitor needs a baseline to measure drift against. Symhas captures the training baseline and configures Model Monitor before production traffic hits the endpoint.
05
Drift-to-Retraining Tested Before Go-LiveAutomatic retraining only matters if it works. Symhas injects a simulated drift event to test the full automation loop before go-live.
06
Data Science Team on SageMaker From Day OneML platform adoption fails when everything changes at once. Symhas onboards data scientists to the same Python and notebook experience they know — MLOps wraps existing workflows.
Also on AWS
Explore the Other AWS Capabilities.
AWS Landing Zone & Migration

The AWS account structure and VPC networking that the SageMaker platform runs within.

Zero-Trust Security on AWS

IAM roles, VPC endpoints, and data security protecting the ML platform and training data.

AWS FinOps & Cost Governance

SageMaker Spot training, managed interruption, and inference endpoint rightsizing.

Next Step
Tell Us Where Your ML Models Are Running Today.
We Will Show You What Production ML Looks Like on AWS.A 30-minute AWS ML assessment with a Symhas SageMaker architect. We will review your model development workflow, data pipeline maturity, and production blockers — and design the MLOps platform that addresses them.
Book an AWS Assessment — 30 min →View Client Outcomes
No commitment. No pitch deck. An honest conversation about your AWS environment.