You will get Deploy Hugging Face Model to Production API (AWS, GCP, RunPod)
Top Rated
Top Rated
Project details
Unlike typical gigs that just run a notebook, I provide architectural-level deployment. I convert raw Hugging Face models into optimized, Dockerized REST APIs (FastAPI) that are ready for real-world traffic. As a Top Rated Plus Solution Architect, I ensure your model is not just "running," but is portable, scalable, and optimized for latency (using ONNX/SGLang where applicable)
I containerize the model using industry-standard base images (NVIDIA/PyTorch). I create a clean REST API interface so your frontend or mobile app can communicate with the model easily.
I deliver docker file with full documentation showing you exactly how to run the API on your machine or cloud server..
I containerize the model using industry-standard base images (NVIDIA/PyTorch). I create a clean REST API interface so your frontend or mobile app can communicate with the model easily.
I deliver docker file with full documentation showing you exactly how to run the API on your machine or cloud server..
Machine Learning Tools
BERT, PyTorchWhat's included
| Service Tiers |
Starter
$200
|
Standard
$300
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 2 days | 2 days | 2 days |
Number of Revisions | 0 | 0 | 0 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 1 | 2 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code | - |
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AL
Adrian L.
Apr 16, 2024
Build AI model & app to detect and read 7-segment digits in photos
MK
Min Suk K.
Apr 3, 2023
Mediapipe for FaceBeautyApp
Daniel's skills are top-notch.
We got a lot of help from him.
And he was always punctual and had a great understanding of the project.
Meeting Daniel was the greatest fortune for us.
Not only our team, but also many other freelance developers who participated in the project.
Daniel's development skills were highly appreciated.
I would like to say thank you again for being with us.
We highly recommend Daniel 120%
We got a lot of help from him.
And he was always punctual and had a great understanding of the project.
Meeting Daniel was the greatest fortune for us.
Not only our team, but also many other freelance developers who participated in the project.
Daniel's development skills were highly appreciated.
I would like to say thank you again for being with us.
We highly recommend Daniel 120%
KC
Ka C.
Feb 17, 2023
ML and Mediapipe Lead Tech Dev for modern Web Application
Very smooth and nice to work with.
BR
Bayan R.
Feb 13, 2023
Need two Python Pytorch tensor functions to be translated/rewritten in Java
Daniel is a great communicator and developer. He does not hesitate to document the code, answer questions, and offers revisions if the outcome is not exactly what anticipated the first time. He is very easy to work with, and I had a great experience.
I will definitely be working with Daniel again in the future.
I will definitely be working with Daniel again in the future.
SW
Simon W.
Sep 12, 2022
Minimal PoC for image search as per our discussion
Thank you for your work.
About Daniel
Senior AI Solutions Architect GPU Scaling on Cloud and On-Device ML
100%
Job Success
Auckland, New Zealand - 5:26 am local time
As a Top Rated Plus expert (Top 3%), I don't just write code—I architect systems that handle 10M+ records with zero downtime. I specialize in taking prototypes from "it works on my machine" to production-grade stability.
🏆 Signature Case Study: AI Company
The Challenge: Scaling complex ML pipelines from scratch.
The Solution: Architected entire GCP infrastructure using Ray Cluster, Pub/Sub, and AlloyDB.
The Result: Successfully scaled from 0 to 200+ GPUs and handled massive concurrency.
Why Clients Hire Me:
End-to-End Vision: From backend orchestration (GCP/Ray) to edge deployment (Android/TFLite 30+ FPS).
Performance Optimization: Proven track record of boosting label matching accuracy from 60% to 87% and achieving 100x speedups via C++ optimization.
Modern "Agentic" Workflow: Leveraging Claude Code and sub-agent architectures to reduce development cycles by 50%—taking you from idea to MVP in record time.
Tech Stack:
ML: Detectron2, YOLOv5/v7, PaddleOCR, MediaPipe, ONNX,Qwen,LLM
Infra: GCP, Ray Cluster, Docker, Kubernetes.
Mobile: Android Native (7+ yrs), TFLite, ncnn.
Steps for completing your project
After purchasing the project, send requirements so Daniel can start the project.
Delivery time starts when Daniel receives requirements from you.
Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Hardware Selection
First, we analyze your specific use case. We select the target model (LLM, CV, NLP) and determine the optimal hardware configuration (GPU VRAM, CUDA version) to ensure the model runs efficiently without overspending on cloud costs.