End-to-End MLOps & Machine Learning Pipeline DevelopmentPankaj Kumar Pramanik
I create production-grade MLOps systems for ML/DL models: data pipelines, training/evaluation, model registry, containerized deployment, and monitoring hooks. You’ll receive a documented, automated setup that’s easy to maintain and ready for cloud deployment.
What's included
MLOps Architecture & Plan
A clear system design covering data flow, training pipeline, model registry/versioning, deployment approach, and monitoring strategy.
Reproducible Training Pipeline
Modular ML/DL training pipeline (data prep → train → evaluate) with configurable parameters and repeatable runs.
Experiment Tracking & Model Versioning
Setup for tracking experiments, metrics, artifacts, and model versions (e.g., MLflow/DVC) to ensure reproducibility.
Model Evaluation Report
Evaluation summary with metrics, validation results, error analysis, and recommended improvements.
Deployment-Ready API (Dockerized)
A production-ready inference API (FastAPI/Flask) packaged with Docker for consistent deployment.
CI/CD Automation
Automated build/test/deploy pipeline (e.g., GitHub Actions) to ship updates reliably and reduce manual work.
Monitoring & Logging Setup
Monitoring-ready integration (logs + metrics + basic drift hooks/alerts) for ongoing reliability in production.
Documentation & Handover
Clear README + runbook (setup, training, deployment, troubleshooting) with a guided handover session.
Starting at$35 /hr
Tags
AWS
FastAPI
Python
scikit-learn
Data Analyst
Data Scientist
ML Engineer
Service provided by
Pankaj Kumar Pramanik proBhandaria Sadar Union, Bangladesh
End-to-End MLOps & Machine Learning Pipeline DevelopmentPankaj Kumar Pramanik
Starting at$35 /hr
Tags
AWS
FastAPI
Python
scikit-learn
Data Analyst
Data Scientist
ML Engineer
I create production-grade MLOps systems for ML/DL models: data pipelines, training/evaluation, model registry, containerized deployment, and monitoring hooks. You’ll receive a documented, automated setup that’s easy to maintain and ready for cloud deployment.
What's included
MLOps Architecture & Plan
A clear system design covering data flow, training pipeline, model registry/versioning, deployment approach, and monitoring strategy.
Reproducible Training Pipeline
Modular ML/DL training pipeline (data prep → train → evaluate) with configurable parameters and repeatable runs.
Experiment Tracking & Model Versioning
Setup for tracking experiments, metrics, artifacts, and model versions (e.g., MLflow/DVC) to ensure reproducibility.
Model Evaluation Report
Evaluation summary with metrics, validation results, error analysis, and recommended improvements.
Deployment-Ready API (Dockerized)
A production-ready inference API (FastAPI/Flask) packaged with Docker for consistent deployment.
CI/CD Automation
Automated build/test/deploy pipeline (e.g., GitHub Actions) to ship updates reliably and reduce manual work.
Monitoring & Logging Setup
Monitoring-ready integration (logs + metrics + basic drift hooks/alerts) for ongoing reliability in production.
Documentation & Handover
Clear README + runbook (setup, training, deployment, troubleshooting) with a guided handover session.
$35 /hr