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You will get machine learning, deep learning, computer vision, aws azure, data analysis
Project details
You will get a production-ready Machine Learning, Deep Learning, Computer Vision, or Data Analysis solution tailored to your business goals. I build scalable AI models, predictive analytics systems, image recognition applications, and cloud-based ML solutions using AWS and Azure. Every project is developed with clean, well-documented code, optimized performance, and a focus on accuracy, scalability, and real-world deployment. Whether you need data preprocessing, model training, AI integration, or end-to-end implementation, I deliver reliable, high-quality solutions that help automate processes, uncover valuable insights, and drive smarter business decisions.
Machine Learning Tools
Amazon SageMaker, ChatGPT, deeplearn.js, GitHub Copilot, GoLearn, Google AutoML, Google Data Studio, Google Sheets, GPT-3, KNIME, Kubeflow, Microsoft CNTK, Microsoft Power BI, MLflow, OpenCV, Python, Sonnet, Vertex AI, Word2vecWhat's included
| Service Tiers |
Starter
$15
|
Standard
$35
|
Advanced
$80
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 8 days |
Number of Revisions | 1 | 4 | Unlimited |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About oluwalegan
AI Automation Engineer | n8n | Claude AI | Zapier | Website Developer
Ondo, Nigeria - 2:59 am local time
I engineer intelligent systems — AI agents, workflow orchestration, and backend infrastructure — that turn fragmented business operations into a single, coordinated ecosystem. The work sits at the intersection of software architecture and applied AI: Claude AI as the reasoning layer, MCP servers as the connective infrastructure, and n8n, Zapier, Python, and JavaScript as the execution layer that carries decisions into action.
What separates production-ready automation from a demo that impresses once and fails under real load is discipline in the underlying design: clear data flow, fault-tolerant orchestration, and interfaces that hold up when business logic changes six months from now. A CRM automation or WhatsApp integration is only as reliable as the system architecture beneath it — so that's where the engineering attention goes first, not last.
Every build starts with a question most freelancers skip: what happens when this breaks, and who inherits the consequences? Systems are designed around that answer — with maintainability, interoperability, and long-term performance treated as requirements, not upsells. Whether the deliverable is a single AI agent, a full workflow automation pipeline, or a custom web platform built on WordPress or Wix with API integrations underneath, the standard is the same: infrastructure a business can depend on, not a script that happens to work today.
This is engineering, applied to operations — not automation as a novelty, but automation as an operational discipline.
Steps for completing your project
After purchasing the project, send requirements so oluwalegan can start the project.
Delivery time starts when oluwalegan receives requirements from you.
oluwalegan works on your project following the steps below.
Revisions may occur after the delivery date.
Project Details & Dataset
Please provide your project goals, dataset (or sample data), preferred ML/DL framework, AWS/Azure requirements, expected outputs, timeline, and any reference files or documentation to get started.