You will get an End-to-End MLOps Pipeline on AWS
Rising Talent
Rising Talent
Project details
I will design and deploy a fully automated MLOps pipeline on AWS, covering data ingestion, model training, versioning, deployment, and monitoring. The pipeline will leverage S3 for storage, SageMaker for training and deployment, CI/CD integration for automation, and monitoring for operational insights.
Machine Learning Tools
Amazon SageMaker, MLflowWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$4,000
|
Advanced
$7,250
|
|---|---|---|---|
| Delivery Time | 12 days | 16 days | 21 days |
Number of Revisions | 2 | 2 | 3 |
Number of Model Variations | 1 | ||
Number of Scenarios | 1 | 2 | |
Number of Graphs/Charts | 2 | 4 | |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | |||
Source Code | - |
About Sean
Senior DevOps and MLOps Engineer | AWS & Kubernetes Expert
Cork, Ireland - 5:50 am local time
✅ 12+ Years in IT | ✅ AWS & Kubernetes Certified | ✅ Infrastructure as Code | ✅ Scalable Cloud & AI Platforms
🔹 About Me
I'm a part-time freelance Platform & DevOps Lead with 12+ years of experience designing, building, and managing mission-critical systems. I specialize in AWS, Kubernetes, GitOps, CI/CD automation, and Infrastructure as Code using tools like Terraform, Helm, and Crossplane.
I’ve led engineering teams, implemented modern DevOps practices, and delivered high-availability platforms with 99.99% uptime. More recently, I’ve expanded into MLOps and AI platform engineering, helping organizations operationalize machine learning workflows, train and deploy models at scale, and build reproducible AI pipelines.
Whether you're starting your cloud journey, modernizing DevOps practices, or building AI-driven applications, I provide scalable, secure, and resilient solutions.
🔹 Cloud & Infrastructure Services
✅ AWS Infrastructure Design & Management
✅ Kubernetes Cluster Setup & Application Deployment
✅ GitOps with ArgoCD & Argo Rollouts
✅ Terraform Infrastructure Automation
✅ Crossplane Integration & Management
✅ Helm Chart Creation & Customization
✅ CI/CD Pipeline Design (GitHub Actions, Azure DevOps, Argo Workflows)
✅ Logging, Monitoring & Alerting (Prometheus, Grafana, Loki, Datadog, CloudWatch)
✅ Disaster Recovery & High Availability Strategies
✅ AWS IAM, Secrets Management (Vault), Security Best Practices
🔹 DevOps Tooling & Automation
✅ End-to-End CI/CD Automation
✅ Containerization with Docker & Kubernetes
✅ Infrastructure as Code (Terraform, Ansible, CloudFormation)
✅ Secrets & Access Management (HashiCorp Vault, HashiCorp Boundary, AWS Secrets Manager)
✅ Policy-as-Code & GitOps Workflows
✅ Scalable Platform Design for Microservices
✅ Automated Image Management & Rollouts
🔹 MLOps & AI Engineering Services
✅ End-to-End MLOps Pipelines (data ingestion, model training, evaluation, deployment)
✅ Model Deployment on Kubernetes
✅ CI/CD for Machine Learning workflows (GitHub Actions, Argo Workflows)
✅ Training Pipelines on AWS SageMaker, EKS, or custom GPU clusters
✅ Model Monitoring & Drift Detection (Prometheus, Grafana)
✅ Reproducible Data Science Environments (Docker, MLflow)
✅ Integration of AI services (AWS Bedrock, OpenAI APIs)
✅ Security & Governance for ML workloads (IAM, secrets management, audit logging)
🔹 Certifications
✅ AWS Certified Solutions Architect – Professional
✅ Certified Kubernetes Administrator (CKA)
✅ Certified Kubernetes Application Developer (CKAD)
✅ AWS Certified SysOps Administrator
✅ AWS Certified Developer – Associate
✅ HashiCorp Certified: Terraform Associate
📍 Based in Cork, Ireland | 🌍 Available for Global Projects
🚀 Let’s streamline your infrastructure, automate your deployments, and bring AI workloads into production at scale!
Steps for completing your project
After purchasing the project, send requirements so Sean can start the project.
Delivery time starts when Sean receives requirements from you.
Sean works on your project following the steps below.
Revisions may occur after the delivery date.
Planning and Requirements Gathering
I’ll define the scope, target ML use cases, dataset locations, model types, and pipeline goals. The storage, compute, and monitoring requirements will be specified, along with access control policies.
AWS Infrastructure Setup
Provision required AWS resources. This will include S3 buckets for raw and processed datasets, SageMaker instances for training and inference, IAM roles for secure access, and ECR for containerized ML models (if applicable).