Production-Grade Infrastructure

MLOps &
Infrastructure
Architecture

I architect infrastructure using cloud and operational pipelines — designing deployment, monitoring, and cost strategies for both short-term efficiency and long-term scalability across enterprise-grade systems.

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mlops-pipeline.system
Code Commit
GitHub · Version Control
↓
CI/CD Pipeline
GitHub Actions · AWS CodePipeline
↓
Container Build
Docker · Kubernetes
↓
Model Deployment
MLflow · Auto-scaling
↓
Monitoring & Drift
Alerting · Retraining
4
Domains
AWS
Cloud
K8s
Orchestration
IaC
Infra Code
What This Is

Infrastructure that
survives production.

MLOps & Infrastructure Architecture is the discipline of making machine learning systems reliable, scalable, and observable in production — not just in development. This work spans the full operational stack: from code commit through CI/CD automation, containerised deployment, and cloud infrastructure, to ongoing monitoring, drift detection, and cost optimisation.

The four core domains — CI/CD Platform Engineering, Container Orchestration, MLOps & ML Systems, and Infrastructure as Code — form an integrated production operating model, not a set of isolated tools. Enterprise-grade systems require all four layers working in concert.

Scale & Depth
4
Core Domains
CI/CD · Containers · MLOps · Infrastructure as Code
AWS
Cloud Platform
CodePipeline · ECS · ECR · CloudFormation · CDK
K8s
Orchestration
Kubernetes · Docker · Service mesh · Auto-scaling
IaC
Infrastructure
Terraform · AWS CDK · CloudFormation · Version-controlled

"Architecting production-grade infrastructure and operational pipelines for enterprise-scale machine learning systems."

Core Areas of Expertise

Production-grade
infrastructure & operations

Four interconnected disciplines that together form a complete MLOps operating model — from the first code commit through automated deployment, container orchestration, infrastructure provisioning, and live production monitoring.

01
CI/CD platform engineering architecture
CI/CD Platform Engineering

Design and implementation of robust CI/CD pipelines for automated testing, building, and deployment of applications and machine learning models.

  • Automated testing and quality gates
  • GitHub Actions and AWS CodePipeline
  • Infrastructure deployment automation
  • Security scanning and compliance checks
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02
Container orchestration architecture
Containers Orchestration

Kubernetes-based container orchestration for scalable, resilient deployment of microservices and machine learning models in production environments.

  • Kubernetes and Docker expertise
  • Auto-scaling and load balancing
  • Service mesh implementation
  • Production-grade configurations
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03
MLOps and machine learning systems architecture
MLOps & ML Systems

End-to-end machine learning operations including experiment tracking, model registry, deployment automation, and production monitoring.

  • MLFlow and experiment tracking
  • Model deployment and serving
  • Performance monitoring and drift detection
  • Automated retraining pipelines
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04
Infrastructure as code architecture
Infrastructure as Code

Programmatic management of cloud infrastructure using Terraform, AWS CDK, and CloudFormation for reproducible, version-controlled environments.

  • Terraform and AWS CDK
  • Multi-environment deployments
  • Cost optimization strategies
  • Security and compliance automation
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Technology Stack

The toolchain behind
scalable ML systems

A complete technical stack spanning cloud infrastructure, container platforms, ML operations, and deployment automation — designed for enterprise-grade production reliability.

CI/CD & Automation
  • GitHub Actions
  • AWS CodePipeline
  • Jenkins
  • Automated quality gates
  • Security scanning
Containers & Orchestration
  • Kubernetes (K8s)
  • Docker & Docker Compose
  • AWS ECS / ECR
  • Service mesh
  • Auto-scaling policies
MLOps & ML Systems
  • MLflow experiment tracking
  • Model registry & versioning
  • Drift detection
  • Automated retraining
  • Model serving & endpoints
Infrastructure as Code
  • Terraform
  • AWS CDK
  • CloudFormation
  • Multi-environment deployments
  • Cost optimization strategies
Engineering Approach

How I architect
production systems

Every MLOps engagement follows a consistent operational discipline — building systems that are observable, reliable, and cost-efficient from the first deployment, not as an afterthought.

Build
Design & Automate the Pipeline
Every deployment starts with a fully automated CI/CD pipeline. Automated testing, quality gates, security scanning, and infrastructure provisioning are embedded from day one — not added later.
GitHub ActionsAWS CodePipelinePytestSonarQube
Deploy
Containerise & Orchestrate at Scale
ML models and services are containerised with Docker and orchestrated via Kubernetes, with auto-scaling, load balancing, and service mesh for production resilience. No manual deployment steps.
DockerKubernetesAWS ECSLoad BalancerService Mesh
Monitor
Track, Detect Drift & Retrain
MLflow tracks every experiment and model version. Production models are monitored for performance degradation and data drift. Automated retraining pipelines are triggered when thresholds are breached.
MLflowModel RegistryDrift DetectionAutomated Retraining
Infrastructure
Provision Infrastructure as Code
All cloud infrastructure — compute, networking, storage, security groups — is version-controlled and reproducible via Terraform and AWS CDK. Multi-environment deployments (dev, staging, production) are identical and auditable.
TerraformAWS CDKCloudFormationMulti-EnvironmentCost Optimisation
Ready to Build

Production infrastructure that
holds up at scale

Architecting production-grade infrastructure and operational pipelines for enterprise-scale machine learning systems — with focus on deployment reliability, monitoring observability, and long-term cost optimisation.

Contact Me View Projects
4
Core Domains
AWS
Cloud Platform
K8s
Orchestration
IaC
Infrastructure Code