Immutable Docker Container
Containerize your existing AI/ML Python script into a robust, OS-agnostic Docker image.
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3 Days
2 Days (...)
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I will deploy, scale, and monitor your AI models using Docker and mlops
Immutable Docker Container
Containerize your existing AI/ML Python script into a robust, OS-agnostic Docker image.
Delivery Time
3 Days
Package Includes: see all
Basic
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Immutable Docker Container
Containerize your existing AI/ML Python script into a robust, OS-agnostic Docker image.
Delivery Time
3 Days
Package Includes: see all
Standard
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Premium
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Service details
Are your AI models bleeding GPU costs, crashing due to dependency conflicts, or silently degrading in production?
THE PROTOTYPE-TO-PRODUCTION CHASM
Data science teams build brilliant algorithms in Jupyter notebooks, but throwing those scripts over the wall to a standard IT team is a recipe for disaster. In 2026, enterprise AI deployment isn't failing because of bad math; it is failing because of infrastructure rot.
Clients come to me facing the same critical bottlenecks:
CUDA Hell: The PyTorch model that worked locally completely breaks on the cloud server due to GPU driver mismatches.
Runaway Inference Costs: Unoptimized models running on idle, highly expensive AWS instances, draining cloud budgets.
Silent Failures: The model doesn't crash, but its accuracy silently drifts over time because no one implemented real-time telemetry.
To run modern machine learning (from custom YOLO vision models to Agentic RAG pipelines) in the real world, you need immutable, cost-optimised engineering.
WHAT I BUILD
I engineer deterministic MLOps architectures. I bridge the gap between data science and DevOps by taking your raw model weights and locking them into highly secure, scalable, and heavily monitored deployment pipelines.
FOUR CORE INFRASTRUCTURE CAPABILITIES:
Immutable Docker Containerization: I containerise your models, locking down every OS-level dependency, tensor library, and routing logic. Your application will run the same on an AWS cluster as it did on the local Linux terminal. Hence, no more environment mismatches.
Cost-Optimised Cloud Inference: Deploying AI shouldn't bankrupt your startup. I architect scalable endpoints using Amazon SageMaker and EKS, configuring precise auto-scaling protocols so you only pay for heavy GPU compute when you actually need it.
Deep MLOps Telemetry: Standard server logs are useless for AI. I instrument your pipelines with MLflow to track inference latency, token consumption, and confidence thresholds, giving you a live dashboard to catch data drift before it impacts your users.
Automated CI/CD for AI: I build secure deployment pipelines. When your data scientists retrain a model, my architecture automatically tests the new weights against safety protocols before seamlessly updating the live cloud endpoint with zero downtime.
As a certified AI engineer who operates natively in Linux terminal environments, I don't guess at server configurations. I build the rigorous, physical architecture your intelligence needs to survive in the real world. Let's stabilise your pipeline.
Key details
- ToolsDockerKubernetesAmazon Eks
- FrameworkNpmTerraform
- Cloud ProvidersGoogle Cloud PlatformAmazon Web Services
- Programming LanguageJavaScriptPython
- ExpertiseMigrationDevelopmentConfiguration
Special note from freelancer
Great AI models are useless if they crash in production. I engineer resilient infrastructure, transforming fragile scripts into scalable, containerized, and heavily monitored cloud deployments using Docker, MLOps, and rigorous safety protocols.
Comparison
Cloud API Deployment
Docker containerization & AWS/GCP cloud deployment as a secure, live REST API endpoint.
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7 Days
4 Days (...)
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Enterprise MLOps Architecture
Enterprise AWS SageMaker/EKS deployment, MLflow tracking, and CI/CD pipeline setup.
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14 Days
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FAQs
- The Basic tier provides the immutable Docker container for your team to deploy. The Standard and Premium tiers include full cloud deployment on AWS or GCP, including setting up the secure REST API endpoints and configuring the necessary GPU/compute instances.
NeilIshu
About NeilIshu
Machine Learning Engineer |Full stack developer |AI Developer
I build intelligent applications using AI/ML and the MERN stack. From machine learning models to full-stack web platforms, I create scalable, efficient, and user-friendly solutions that turn ideas into powerful digital products.
I build intelligent applications using AI/ML and the MERN stack. From machine learning models to full-stack web platforms, I create scalable, efficient, and user-friendly solutions that turn ideas into powerful digital products.
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