You will get a build and deploy an MLOps pipeline to production
Project details
I will build and deploy a complete MLOps pipeline tailored to your use case, from data to production. With hands-on experience in MLflow, Docker, CI/CD, and cloud platforms like GCP and AWS, I deliver scalable, reproducible, and monitored pipelines. Whether you're starting from scratch or want to improve your current ML workflow, I can help you reach production-ready maturity.
Machine Learning Tools
Amazon SageMaker, BERT, ChatGPT, GitHub Copilot, Google Data Studio, GPT-3, Keras, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$70
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $70
Additional Revision
+$20
Additional Model Variation
(+ 1 Day)
+$40Frequently asked questions
About Arnaud
Python Developer | Data Analysis & ML Automation
Selestat, France - 3:12 pm local time
I build clean and fast scripts to help you:
• Extract and process data (CSV, JSON, APIs, web scraping)
• Automate repetitive tasks and workflows
• Build small dashboards or reporting tools (Streamlit, CLI)
I work with Pandas, Requests, Streamlit, and Python standard libraries to deliver production-ready solutions with documentation.
If you're looking for a fast and reliable script — I can help. Let's connect!
Steps for completing your project
After purchasing the project, send requirements so Arnaud can start the project.
Delivery time starts when Arnaud receives requirements from you.
Arnaud works on your project following the steps below.
Revisions may occur after the delivery date.
Project scoping and planning
We'll review your use case, define objectives, align on tools (e.g., MLflow, Docker, GCP, etc.), and outline the MLOps workflow.
Pipeline development
I'll build and document the pipeline: data preprocessing, model training, evaluation, versioning, and deployment orchestration.