You will get and ML model monitoring and drift detection for production
Project details
A model that was accurate at deployment may be silently wrong today. Data drift is the most common and most expensive way ML systems fail in production. Most teams don't find out until a business metric drops.
I've deployed PSI-based drift monitoring on a system handling 500,000+ monthly predictions, detecting a critical drift event and triggering retraining within 2 days mitigating ~$40,000/month in revenue risk from degraded model accuracy.
What makes this project different:
— Feature-level PSI monitoring: each feature tracked individually so you know exactly what shifted and when
— Three-tier alerting: green, amber, and red thresholds with automatic email or Slack alerts
— MLflow model registry: full version history with one-click rollback if a retrained model underperforms
— Streamlit health dashboard: live model health, 30-day rolling PSI trends, and prediction volume
This is a full MLOps layer that keeps your model accurate without manual intervention. IBM ML Professional Certificate holder. Deployed production ML systems across fintech, telecom, and SaaS. If you have a model in production and no visibility into whether it still works this project is for you.
I've deployed PSI-based drift monitoring on a system handling 500,000+ monthly predictions, detecting a critical drift event and triggering retraining within 2 days mitigating ~$40,000/month in revenue risk from degraded model accuracy.
What makes this project different:
— Feature-level PSI monitoring: each feature tracked individually so you know exactly what shifted and when
— Three-tier alerting: green, amber, and red thresholds with automatic email or Slack alerts
— MLflow model registry: full version history with one-click rollback if a retrained model underperforms
— Streamlit health dashboard: live model health, 30-day rolling PSI trends, and prediction volume
This is a full MLOps layer that keeps your model accurate without manual intervention. IBM ML Professional Certificate holder. Deployed production ML systems across fintech, telecom, and SaaS. If you have a model in production and no visibility into whether it still works this project is for you.
Machine Learning Tools
Apache Spark, Azure Machine Learning, Microsoft Excel, Microsoft Power BI, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, Tableau, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$500
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 14 | 2 | 3 |
Number of Graphs/Charts | 4 | 6 | 8 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - |
About Imeobong
Data Scientist | Causal ML , MLOps , LLM Pipelines , Power BI
Uyo, Nigeria - 9:41 pm local time
Over 3+ years, I've delivered 30+ end-to-end data solutions for clients in fintech, telecom, media, healthcare, and real estate. My work has projected $516K+ in combined revenue outcomes:
→ Churn prediction model (AUC 0.74 → 0.79) across 250K users — ~$180K annual revenue preserved
→ A/B experimentation platform across 120K users — ~$336K annualised revenue lift
→ PSI-based ML drift detection on 500K+ monthly predictions — ~$40K/month revenue-at-risk mitigated
→ ETL pipeline automation — 60% faster runtime, 70% faster client reporting
WHAT I BUILD:
🔹 Machine Learning & AI
Churn prediction, causal uplift modelling (S/T/X-Learner meta-algorithms, IPTW), A/B testing, NLP pipelines, LLM orchestration, real-time signal detection. Evaluated with rigorous metrics (AUC, Qini, AUUC).
🔹 Data Engineering
Apache Airflow, Apache Kafka, ETL pipelines, dbt, BigQuery, PostgreSQL, MySQL. I build pipelines that don't fail — 99.9% ingestion success on 80K–150K daily records.
🔹 MLOps & Deployment
MLflow experiment tracking, Docker containerisation, FastAPI microservice APIs, PSI drift monitoring with automated retraining triggers. Models don't just train — they ship and stay accurate.
🔹 Business Intelligence
Power BI dashboards with DAX, Looker Studio, Streamlit — translating raw data into decisions executives can act on.
TECH STACK:
Python · SQL · scikit-learn · XGBoost · LightGBM · MLflow · Apache Airflow · Apache Kafka · PostgreSQL · BigQuery · FastAPI · Docker · Streamlit · Power BI · AWS · GCP · dbt
I hold an IBM Machine Learning Professional Certificate (IBM/Coursera, 2026) and a Certified Data Scientist credential (DataCamp, 2026), with a B.Sc. in Statistics.
Available for full-time, part-time, and contract remote engagements. Let's talk about what your data can do.
Steps for completing your project
After purchasing the project, send requirements so Imeobong can start the project.
Delivery time starts when Imeobong receives requirements from you.
Imeobong works on your project following the steps below.
Revisions may occur after the delivery date.
Baseline audit and setup
I review your model, training data, and prediction logs, calculate baseline feature distributions, and identify which features carry the highest drift risk based on their variance in production.
PSI monitoring and alert logic
I build the PSI calculation pipeline that runs on your prediction data, defines green, amber, and red thresholds per feature, and fires alerts via your chosen channel when drift is detected.