You will get ML Model Audit & Optimization — Performance, Explainability & Deployment
Project details
Already have a model but it's underperforming, overfitting, or stuck in a notebook with no path to production?
I audit existing ML pipelines and deliver concrete improvements: fixing data leakage, reducing overfitting, optimizing classification thresholds, adding SHAP-based explainability, or wrapping your model in a FastAPI REST API ready for deployment.
You share your code and data. I deliver a corrected, documented, and improved pipeline — plus a written report explaining exactly what was wrong and what was fixed.
Ideal for teams that built a first version and need a senior eye to take it further.
I audit existing ML pipelines and deliver concrete improvements: fixing data leakage, reducing overfitting, optimizing classification thresholds, adding SHAP-based explainability, or wrapping your model in a FastAPI REST API ready for deployment.
You share your code and data. I deliver a corrected, documented, and improved pipeline — plus a written report explaining exactly what was wrong and what was fixed.
Ideal for teams that built a first version and need a senior eye to take it further.
Machine Learning Tools
MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$250
|
Standard
$450
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $150
Additional Revision
+$50Frequently asked questions
About Mickel
ML Engineer | Fraud Detection | Credit Risk | Predictive Modeling | ML
Santo Domingo Oeste, Dominican Republic - 9:02 pm local time
I've developed a fraud detection system on the IEEE-CIS dataset using LightGBM, with a full MLOps pipeline: feature engineering with ColumnTransformer, hyperparameter tuning via Optuna, experiment tracking in MLflow, threshold optimization, and SHAP-based explainability — all deployed via FastAPI. In parallel, I've worked on credit default risk modeling (Home Credit dataset), focused on maximizing AUC-PR under real-world class imbalance conditions.
I work in Python-first environments and deliver clean, reproducible, documented pipelines — not just notebooks.
Core stack: Python · LightGBM · XGBoost · Scikit-learn · FastAPI · MLflow · Optuna · SHAP · Pandas · SQL
If you need a model that actually works in production — let's talk."
Steps for completing your project
After purchasing the project, send requirements so Mickel can start the project.
Delivery time starts when Mickel receives requirements from you.
Mickel works on your project following the steps below.
Revisions may occur after the delivery date.
Code & Pipeline Review
Analyze the client's existing notebook, data, and model for issues.
Diagnosis Report
Document all findings: overfitting, leakage, wrong metrics, or poor features.