You will get ML on K8s (prem and cloud)
Project details
This project delivers a fully automated, production-grade Machine Learning pipeline running on Kubernetes (K8s), built for both on-premises and cloud environments. What sets my work apart is the complete end-to-end platform: scalable ML pipeline orchestration, secure storage with Rook/Ceph, integrated monitoring, databases, and hardened infrastructure components. Security and data privacy are top priorities - implementing encryption at rest and in transit, RBAC, secrets management, network policies, and best-practice isolation for sensitive workloads. I also provide a custom Go-based installer (CLI) that automates deployment and ensures consistent, secure configurations across environments. The result is a robust, repeatable, enterprise-grade ML platform engineered for reliability, compliance, and long-term operation.
Machine Learning Tools
Databricks MLflow, Kubeflow, MLflow, NVIDIA AI Platform, PyTorch, Sonnet, TensorFlowWhat's included
| Service Tiers |
Starter
$2,900
|
Standard
$4,500
|
Advanced
$8,500
|
|---|---|---|---|
| Delivery Time | 15 days | 30 days | 60 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Model Validation/Testing | - | - | |
Model Documentation | - | - | |
Data Source Connectivity | - | - | |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 10 Days)
+$5,000About Robinson
Full Stack/Cloud/DevOps/ML Engineer
Lysaker, Norway - 8:00 am local time
Steps for completing your project
After purchasing the project, send requirements so Robinson can start the project.
Delivery time starts when Robinson receives requirements from you.
Robinson works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Architecture Review
I review your current infrastructure, ML workflow requirements, data sources, and deployment environment (cloud, on-prem, or hybrid). Outcome: A finalized architecture and integration plan.
Kubernetes Environment Preparation
Set up or validate Kubernetes clusters (cloud-managed or on-prem). Configure namespaces, RBAC, networking, storage classes, and CRDs required for ML operations.