You will get Train, Fine-Tune & Deploy a Custom Python ML Model for Production
Project details
Most ML projects die between the notebook and production. The model works locally, the data pipeline breaks on real input, and nobody can actually use it. I close that gap.
I train, fine-tune, and deploy custom machine learning models — classification, regression, NLP, tabular, embeddings — and hand them over as working production systems with a documented API and a live deployment. Not a .pkl file and good luck.
What I build:
Custom ML models trained on your data — scikit-learn, PyTorch, HuggingFace
Full preprocessing pipelines — cleaning, feature engineering, normalization, augmentation
Model evaluation reports — accuracy, F1, ROC-AUC, confusion matrix, full breakdown
FastAPI backend wrapping the model — REST endpoints, documented, ready to integrate
Full web deployment
Fine-tuning of pre-trained models on domain-specific data (BERT, ResNet, EfficientNet, custom)
My proof: I built a face classification ensemble (EfficientNetB0 + ResNet50) that hit 99.22% accuracy with Grad-CAM explainability — trained, evaluated, and deployed with a full inference API. I built a UFC fight outcome prediction on real matches. Same engineering discipline applied to every client project.
I train, fine-tune, and deploy custom machine learning models — classification, regression, NLP, tabular, embeddings — and hand them over as working production systems with a documented API and a live deployment. Not a .pkl file and good luck.
What I build:
Custom ML models trained on your data — scikit-learn, PyTorch, HuggingFace
Full preprocessing pipelines — cleaning, feature engineering, normalization, augmentation
Model evaluation reports — accuracy, F1, ROC-AUC, confusion matrix, full breakdown
FastAPI backend wrapping the model — REST endpoints, documented, ready to integrate
Full web deployment
Fine-tuning of pre-trained models on domain-specific data (BERT, ResNet, EfficientNet, custom)
My proof: I built a face classification ensemble (EfficientNetB0 + ResNet50) that hit 99.22% accuracy with Grad-CAM explainability — trained, evaluated, and deployed with a full inference API. I built a UFC fight outcome prediction on real matches. Same engineering discipline applied to every client project.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, BERT, ChatGPT, Databricks MLflow, GitHub Copilot, Google AutoML, GPT-3, Keras, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, Sonnet, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$150
|
Standard
$200
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 2 | 3 | Unlimited |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Grad-CAM / Explainability Layer
(+ 1 Day)
+$30Frequently asked questions
About Karim
AI/ML Engineer
Cairo, Egypt - 8:24 am local time
I am a Machine Learning Engineer specializing in building AI-powered SaaS products from architecture to deployment. I design production-ready systems that are secure, scalable, and maintainable, helping founders and teams turn AI concepts into real applications.
I build:
• Retrieval-Augmented Generation (RAG) systems connected to proprietary data
• AI assistants with memory, tool-calling, and structured workflows
• Document Q&A and knowledge base platforms
• Resume analysis and semantic matching systems
• Custom LLM pipelines and AI automation backends
Tech stack includes FastAPI, Next.js, Supabase, PostgreSQL, vector databases (pgvector, Pinecone, Qdrant), OpenAI, and Claude APIs.
Every project includes clean architecture, secure authentication, API documentation, and deployment setup. My focus is building reliable backend systems that power real AI products — not experimental prototypes.
If you're building a serious AI product or SaaS MVP, I can help you architect and deliver it properly.
Steps for completing your project
After purchasing the project, send requirements so Karim can start the project.
Delivery time starts when Karim receives requirements from you.
Karim works on your project following the steps below.
Revisions may occur after the delivery date.
Data review & scope confirmation
Review dataset and problem statement, confirm model type, accuracy targets, and deliverables before starting.
Data preprocessing & pipeline setup
Clean, normalize, and engineer features from your dataset. Handle missing values, imbalances, and formatting.