I will be your machine learning in production mlops engineer

Getting a model to work in a notebook is one thing getting it to run reliably in production is another. I help close that gap.

I can help you:

• Deploy ML models to production (FastAPI, Docker, AWS EC2/Elastic Beanstalk, TensorFlow Serving)
• Design rollout strategies shadow mode, canary releases, blue-green deployment, with rollback plans
• Set up monitoring for software, input, and output metrics to catch data/concept drift and pipeline failures early
• Run error analysis to prioritize fixes that actually improve real-world performance, not just test-set accuracy
• Build and audit data pipelines cleaning, labeling consistency, train/dev/test splits, and data provenance tracking
• Establish performance baselines using human-level performance (HLP) and set up experiment tracking for reproducible results

Whether you're taking a proof-of-concept to deployment, need monitoring for an existing model, or want a full pipeline audit (accuracy, fairness, bias included), I bring a structured, end-to-end approach across the ML lifecycle scoping, data, modeling, deployment, and maintenance.

Send me your project details and let's get your model shipped and running smoothly.