You will get deployed ML Model as REST API
Rising Talent
Project details
You've done the hard part — training the model. Getting it into production is where most teams get stuck. I specialize in exactly that gap: wrapping ML models into scalable FastAPI services, containerized with Docker and deployed on Azure, with full documentation and support.
Machine Learning Tools
Apache Spark, Apache Spark MLlib, Azure Machine Learning, Databricks MLflow, Kubeflow, MLflow, NumPy, NVIDIA AI Platform, Open Neural Network Exchange, pandas, Python Scikit-Learn, PyTorch, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$199
|
Standard
$449
|
Advanced
$899
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | Unlimited |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 0 | 3 | 5 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $20
Additional Revision
+$5
Additional Model Variation
(+ 2 Days)
+$15
Additional Graph/Chart
(+ 1 Day)
+$5
Model Validation/Testing
(+ 1 Day)
+$10
Data Source Connectivity
(+ 2 Days)
+$20Frequently asked questions
1 review
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RA
Rahul A.
Apr 5, 2026
Data Scientist / Analytics Developer for Web-Based Visualizations
We had a great experience working with Hari. He was open to exploring ideas and delivered on time. Highly recommended.
About Hari
MLOps & RAG Engineer | LLM Deployment | FastAPI | Docker | LangChain
Noida, India - 6:38 am local time
I'm Hari — an MLOps and AI engineer who builds the full pipeline around your model: containerization, CI/CD, monitoring, RAG systems, and LLM deployment. One completed project, one 5-star review — and I'm looking for the next client who needs this done right.
What I've shipped:
SmartMeter Analytics Platform — Built a full-stack system processing 15-minute interval data from 36 IoT meters. FastAPI backend running live anomaly detection (Z-score), 24-hour load forecasting, and voltage quality analysis on PostgreSQL data — delivered to a 13-page React dashboard with real-time KPIs and solar simulation.
End-to-End MLOps Pipeline — Designed and deployed three production ML models (flight price prediction, gender classification, hotel recommendations) with full experiment tracking via MLflow, a Flask REST API, Docker containerization, Kubernetes auto-scaling, Airflow data pipelines, and Jenkins CI/CD.
My production stack:
Python | FastAPI | Docker | Kubernetes | MLflow | Apache Airflow | LangChain | PostgreSQL | CI/CD | RAG pipelines | TensorFlow | PyTorch
Where I add the most value:
- Deploying your existing ML model as a scalable REST API
- Building RAG systems and LLM-powered applications with LangChain
- Setting up MLOps infrastructure (experiment tracking, model versioning, retraining pipelines)
- Migrating data pipelines from notebooks to production-grade Airflow workflows
If you're building something in this space, tell me what you're working on. I'll give you a direct answer within 24 hours on whether and how I can help — no fluff, no commitment needed.
Steps for completing your project
After purchasing the project, send requirements so Hari can start the project.
Delivery time starts when Hari receives requirements from you.
Hari works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Model Review
I review your model file, preprocessing code, and sample inputs/outputs to fully understand the structure before writing any code.
API Design & Architecture
I design the FastAPI structure — endpoints, request/response schema, validation rules, and error handling — tailored to your model.