You will get Deployable ML API (Flask) — model endpoint + Docker + basic tests
Project details
I’ll wrap your ML model into a Flask-based web service, add a production WSGI setup (Gunicorn), Dockerfile, basic tests and deployment notes. Ideal for prototypes that need a stable, reproducible inference endpoint and optional simple dashboard or health checks.
What's included
| Service Tiers |
Starter
$80
|
Standard
$225
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 2 days | 7 days | 30 days |
Number of Revisions | 0 | 0 | 0 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$200 - $1,000
Additional Revision
+$50
Additional Model Variation
(+ 7 Days)
+$100
Additional Scenario
(+ 7 Days)
+$80
Additional Graph/Chart
(+ 5 Days)
+$82
Model Validation/Testing
(+ 5 Days)
+$80
Model Documentation
(+ 10 Days)
+$250
Source Code
(+ 10 Days)
+$150Frequently asked questions
About Marcone
AI & ML Engineer Python Flask Docker NLP Computer Vision
Natal, Brazil - 1:58 am local time
What I can do for you (quick bullets)
End-to-end ML APIs (FastAPI + Docker + PostgreSQL)
NLP solutions: text classification, entity extraction, LLM integration (OpenAI + fine-tuning)
Data engineering: ETL, cleaning, Pandas / Spark scripts, data pipelines
Computer vision: object detection, inference on Jetson/RPi, demo dashboards
Quick automations and scripting to save manual work (scraping, reports, integration)
Why hire me
Hands-on experience shipping ML prototypes to production.
Practical focus: simple, maintainable solutions (Clean Code + tests).
Good English for technical collaboration; fast iteration and clear docs/README.
Steps for completing your project
After purchasing the project, send requirements so Marcone can start the project.
Delivery time starts when Marcone receives requirements from you.
Marcone works on your project following the steps below.
Revisions may occur after the delivery date.
ML endpoint served via Flask, containerized and ready to deploy.
ML model into a Flask-based web service, add a production WSGI setup (Gunicorn), Dockerfile, basic tests and deployment notes. Ideal for prototypes that need a stable, reproducible inference endpoint and optional simple dashboard or health checks.