---
title: "I will build and deploy custom machine learning models and MLOps pipelines — per hour"
url: https://zinnhub.com/zinns/i-will-build-and-deploy-custom-machine-learning-models-and-mlops-pipelines-per-hour/
description: "End-to-end machine learning engineering — from data preprocessing and model training to cloud deployment on GCP and Hugging Face. Deep learning, computer vision, NLP, predictive modelling, and time-series forecasting, all handled across the full MLOps lifecycle."
published: 2026-03-19T12:34:42+00:00
modified: 2026-03-19T12:34:42+00:00
language: en-GB
---

# I will build and deploy custom machine learning models and MLOps pipelines — per hour

I build and deploy complete, production-ready machine learning systems that solve real business problems — not just prototypes that sit in a notebook gathering dust.

With 15 years of professional experience as a creative lead before transitioning into machine learning engineering, I bring something most ML engineers don’t: a strategic, business-first perspective. I don’t just build models — I understand why you need them and what they need to achieve commercially. That means every system I deliver is designed with your actual operational goals in mind, not just technical accuracy metrics.

My expertise spans the entire MLOps lifecycle. I handle everything from raw data engineering and feature extraction through to model training, evaluation, and full CI/CD deployment on Google Cloud Platform or Hugging Face. You don’t need to hire separate people for data prep, modelling, and deployment — I deliver the complete pipeline.

My core specialisms include predictive modelling for forecasting business outcomes, computer vision and deep learning using convolutional neural networks, natural language processing for text analysis and language understanding, time-series forecasting for demand planning and operational prediction, and GenAI applications including RAG pipelines and LangChain integrations.

Real projects I’ve built and deployed include an anomaly detection system using autoencoders and mel spectrograms for predictive maintenance (shortlisted at ITEC Beijing), an end-to-end migraine prediction app deployed on Hugging Face, a restaurant demand forecasting model for inventory optimisation, a CNN-based plant disease classifier deployed on GCP with a live frontend, and an AI-powered homework grading application for the education sector.

My tech stack includes Python, TensorFlow, Keras, Scikit-Learn, Pandas, NumPy, MLflow, Docker, GitHub Actions, GCP, and Hugging Face — everything needed to take a concept from raw data to a live, scalable, deployed system.

This Zinn is priced per hour. Please message me before ordering so we can scope your project, estimate the hours required, and make sure we’re aligned on deliverables before you commit. Then simply select the number of hours as your quantity at checkout.

Let’s turn your data into a deployed solution that actually works.