You will get your ML model deployed as a production-ready REST API
Project details
I take your trained machine learning model and deploy it as a production-ready API that your applications can call in real time. Whether you need a simple Docker container, a cloud-hosted endpoint on AWS SageMaker, or a full MLOps pipeline with CI/CD and monitoring, I deliver a system that's reliable, scalable, and well-documented.
I've deployed models using FastAPI, Flask, Docker, AWS SageMaker, and GitHub Actions CI/CD with Prometheus and Grafana monitoring. I treat deployment as engineering, not an afterthought.
I've deployed models using FastAPI, Flask, Docker, AWS SageMaker, and GitHub Actions CI/CD with Prometheus and Grafana monitoring. I treat deployment as engineering, not an afterthought.
Machine Learning Tools
Amazon SageMaker, Apache MXNet, Apache Spark, Apache Spark MLlib, Azure Machine Learning, BigDL, ChatGPT, Databricks Platform, Databricks MLflow, Google AutoML, Google Data Studio, Google Sheets, Keras, Kubeflow, MATLAB, Microsoft CNTK, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, Vertex AIWhat's included
| Service Tiers |
Starter
$200
|
Standard
$500
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 6 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Extra Cloud Region
(+ 2 Days)
+$100
Load Testing Report
(+ 1 Day)
+$75
Authentication Layer
(+ 2 Days)
+$100Frequently asked questions
About Orbin
AI/ML Engineer | Deep Learning, NLP & Computer Vision | End-to-End Mod
Kozhikode, India - 12:54 pm local time
My strongest areas include NLP (sentiment analysis, text generation, sequence-to-sequence models), computer vision (CNNs, CycleGANs), and classical ML (regression, classification, clustering, time series forecasting). I've built projects using PyTorch, TensorFlow, Scikit-Learn, LangChain, and PySpark, and I'm comfortable working with tools like Docker, GitHub Actions, and cloud services for MLOps workflows.
Some highlights from my project work: a real-time NLP pipeline for financial news intelligence using BERT, LDA, and Transformer summarization; a CycleGAN for unpaired face sketch-to-photo translation; a full CI/CD ML API with Prometheus/Grafana monitoring; and an Apache Spark ETL pipeline for churn prediction. I focus on writing clean, well-documented code and delivering results that are practical and measurable.
I'm responsive, detail-oriented, and genuinely passionate about solving problems with data. If your project involves building, improving, or deploying an ML/DL system, I'd love to hear about it.
Steps for completing your project
After purchasing the project, send requirements so Orbin can start the project.
Delivery time starts when Orbin receives requirements from you.
Orbin works on your project following the steps below.
Revisions may occur after the delivery date.
Model Assessment
Review your model, dependencies, and performance requirements
API Development
Build REST API with FastAPI or Flask, including input validation and error handling