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
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
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
Azure Data & AI Engineer | Databricks, Fabric Lakehouse & AI/MLOps
Noida, India - 4:12 pm local time
If I do not deliver the agreed project scope to your exact satisfaction, you get a 100% full refund — no questions asked.
I am Hari — an Azure Data & AI Engineer with 3.5+ years of experience and an MSc in CS (AI/ML). I turn messy data sources into fast, reliable cloud lakehouses and production-ready AI pipelines.
What I Deliver:
- Azure Lakehouse Architecture: End-to-end Medallion pipelines using Azure Databricks (PySpark), Delta Lake, and Microsoft Fabric.
- AI/ML & MLOps Data Infrastructure: Designing data pipelines optimized for model training, feature stores, RAG/LLMs, and automated retraining.
- Resilient ETL/ELT Automation: Production-grade data pipelines built with Python, SQL, Apache Airflow, and Apache Kafka.
- Query & Cost Optimization: Tuning slow SQL queries and cluster configurations (boosted query execution by 45% in prior production roles).
Stack:
Azure (ADLS, Data Factory, Synapse), Databricks, Fabric, Python, Advanced SQL, Airflow, Kafka, Docker, PostgreSQL, MongoDB, AIML, MLOps,
Building a data platform or AI pipeline?
👉 Drop me a message. I will review your architecture and send you a technical roadmap within 24 hours.
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.