Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/raj200501/GoogleCloud-Federated-ML-Pipeline
Deploys a scalable and automated federated machine learning pipeline on Google Cloud Platform (GCP). Uses resources such as Google Cloud Storage buckets for data storage, AI Platform for federated model training, and Cloud Run for model deployment, with built-in capabilities to ensure data privacy and security.
https://github.com/raj200501/GoogleCloud-Federated-ML-Pipeline
Last synced: about 2 months ago
JSON representation
Deploys a scalable and automated federated machine learning pipeline on Google Cloud Platform (GCP). Uses resources such as Google Cloud Storage buckets for data storage, AI Platform for federated model training, and Cloud Run for model deployment, with built-in capabilities to ensure data privacy and security.
- Host: GitHub
- URL: https://github.com/raj200501/GoogleCloud-Federated-ML-Pipeline
- Owner: raj200501
- License: mit
- Created: 2024-07-14T07:42:02.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-07-14T07:52:01.000Z (3 months ago)
- Last Synced: 2024-07-14T08:48:18.984Z (3 months ago)
- Language: HCL
- Size: 26.4 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-MLSecOps - GoogleCloud-Federated-ML-Pipeline
README
# GCPFederatedMLPipeline
## Overview
**GCPFederatedMLPipeline** is an unprecedented Terraform project that revolutionizes the deployment of scalable, automated, and secure federated machine learning pipelines on Google Cloud Platform (GCP). This cutting-edge project seamlessly integrates a suite of GCP services to orchestrate a comprehensive machine learning workflow, encompassing data storage, model training, and real-time deployment. Designed with meticulous attention to privacy, security, and performance, **GCPFederatedMLPipeline** sets a new benchmark in cloud-based machine learning.
## Key Features
- **Seamless Data Management**: Efficiently handle data storage and retrieval using Google Cloud Storage, ensuring optimal performance and security.
- **Advanced Federated Learning**: Employ AI Platform to conduct federated learning, safeguarding data privacy while leveraging distributed datasets for robust model training.
- **Real-Time Deployment**: Deploy trained models on Cloud Run, enabling scalable and low-latency predictions accessible through a secure, serverless platform.
- **Comprehensive IAM Policies**: Implement robust IAM roles and policies to enforce fine-grained access control and ensure compliance with security best practices.
- **Modular Architecture**: Designed for extensibility and scalability, this project allows seamless integration and expansion to accommodate evolving machine learning needs.
## The Visionary Behind the Project
This project epitomizes the vision and technical prowess of a transformative leader in cloud computing and machine learning. By pioneering the integration of federated learning within a fully automated GCP infrastructure, this project not only addresses critical data privacy concerns but also democratizes access to advanced machine learning capabilities. With **GCPFederatedMLPipeline**, we step into a new era of intelligent, secure, and scalable cloud solutions that empower organizations to harness the full potential of their data without compromising privacy or security.
## Prerequisites
- **Terraform**: Ensure Terraform is installed on your local machine.
- **Google Cloud SDK**: Install and configure Google Cloud SDK with appropriate access credentials.
- **GCP Project**: Set up a GCP project with billing enabled and necessary APIs activated.
## Installation
### Clone the Repository
```sh
git clone https://github.com/your-username/GCPFederatedMLPipeline.git
cd GCPFederatedMLPipeline
```
### Initialize Terraform
```sh
terraform init
```
### Apply the Terraform Configuration
```sh
terraform apply
```
## Architecture
### Main Components
- Google Cloud Storage: Facilitates secure and scalable storage of training data and model artifacts.
- AI Platform: Powers federated learning to train machine learning models across distributed data sources while preserving data privacy.
- Cloud Run: Provides a serverless environment for deploying and serving trained models, ensuring high availability and low latency.
- IAM Roles and Policies: Implements rigorous security controls to manage access and permissions effectively.
### Workflow
- Provision Infrastructure: Utilize Terraform to deploy the necessary GCP resources.
- Data Management: Store training data securely in Google Cloud Storage.
- Federated Training: Execute federated learning workflows on AI Platform.
- Model Deployment: Deploy the trained model to Cloud Run for real-time inference.
## Usage
### Configure the Project
#### Edit the Configuration File:
```sh
Update config/config.yaml
```
with your GCP project details and resource names.
### Run Federated Training
#### Execute the Training Script:
```sh
./scripts/federated_training.sh
```
### Deploy the Model
#### Build the Model Docker Image:
```sh
./build/build.sh
```
#### Deploy the Model:
```sh
./deploy/deploy.sh
```
## License
This project is licensed under the MIT License - see the LICENSE file for details.