The MLOps Pipeline KickstartAli Beheshti
Stop letting your AI experiments vanish into unorganized notebooks. I help data teams transition from manual, high-risk workflows to production-grade MLOps pipelines. This service builds the "Traceability Skeleton" your business needs to ensure that every model you build is 100% reproducible, auditable, and ready for the real world.
What’s Included (The Deliverables)
I will implement a standard, scalable foundation for one of your core ML projects within 14 days:
Data Lineage Setup: Full implementation of DVC to version your datasets, ensuring you never lose track of which data produced which model.
Experiment Dashboard: Integration of MLflow to track every hyperparameter, metric, and training run visually.
Validation Gates: Custom Python scripts to automatically audit your data for "silent failures" (nulls, schema shifts) before training begins.
Infrastructure as Code: A tailored Dockerfile and CI/CD configuration (GitHub Actions) to ensure your model runs perfectly on any server, every time.
The Process
Audit: A 60-minute deep dive into your current data stack and business goals.
Scaffolding: I build the directory structure and initialize your Git/DVC repositories.
Pipeline Construction: I write the
dvc.yaml and train.py logic to automate your specific workflow.Handover: A final walkthrough and documentation so your team can run
dvc repro with total confidence.Who This is For
Startups moving their first model from research to a live app.
Lean Tech Teams who need DevOps rigor without hiring a full-time engineer.
Legacy Enterprises modernizing CSV/Excel-based data workflows into professional pipelines.
Ali's other services
Starting at$1,500
Duration2 weeks
Tags
Docker
Python
Data Engineer
Data Engineer
Artificial Intelligence
DevOps Engineering
MLflow, DVC
MLOps, CI/CD
Software Engineering
Service provided by
Ali Beheshti Herndon, USA
The MLOps Pipeline KickstartAli Beheshti
Starting at$1,500
Duration2 weeks
Tags
Docker
Python
Data Engineer
Data Engineer
Artificial Intelligence
DevOps Engineering
MLflow, DVC
MLOps, CI/CD
Software Engineering
Stop letting your AI experiments vanish into unorganized notebooks. I help data teams transition from manual, high-risk workflows to production-grade MLOps pipelines. This service builds the "Traceability Skeleton" your business needs to ensure that every model you build is 100% reproducible, auditable, and ready for the real world.
What’s Included (The Deliverables)
I will implement a standard, scalable foundation for one of your core ML projects within 14 days:
Data Lineage Setup: Full implementation of DVC to version your datasets, ensuring you never lose track of which data produced which model.
Experiment Dashboard: Integration of MLflow to track every hyperparameter, metric, and training run visually.
Validation Gates: Custom Python scripts to automatically audit your data for "silent failures" (nulls, schema shifts) before training begins.
Infrastructure as Code: A tailored Dockerfile and CI/CD configuration (GitHub Actions) to ensure your model runs perfectly on any server, every time.
The Process
Audit: A 60-minute deep dive into your current data stack and business goals.
Scaffolding: I build the directory structure and initialize your Git/DVC repositories.
Pipeline Construction: I write the
dvc.yaml and train.py logic to automate your specific workflow.Handover: A final walkthrough and documentation so your team can run
dvc repro with total confidence.Who This is For
Startups moving their first model from research to a live app.
Lean Tech Teams who need DevOps rigor without hiring a full-time engineer.
Legacy Enterprises modernizing CSV/Excel-based data workflows into professional pipelines.
Ali's other services
$1,500