Cloud Computing
The Zynthetix platform utilizes various cloud computing services to ensure scalability, reliability, and performance. This section provides an overview of the key cloud computing technologies used in the platform.
AWS EC2
Amazon Elastic Compute Cloud (EC2) provides scalable virtual servers for running applications. It is used to deploy and run the backend and machine learning models.
Key Features
- Scalability: Easily scale instances up or down based on demand.
- Variety: Wide range of instance types to suit different workloads.
- Security: Provides robust security features including VPC, IAM, and security groups.
AWS S3
Amazon Simple Storage Service (S3) is an object storage service used for storing and retrieving data. It is used to store datasets, synthetic data, and other static assets.
Key Features
- Durability: Highly durable storage with multiple copies of data.
- Scalability: Unlimited storage capacity to handle large datasets.
- Accessibility: Accessible from anywhere with high availability.
AWS SageMaker
Amazon SageMaker is a fully managed service for building, training, and deploying machine learning models. It is used to manage the machine learning lifecycle in Zynthetix.
Key Features
- Model Training: Provides scalable infrastructure for training machine learning models.
- Deployment: Deploys models to production with built-in scalability and monitoring.
- Integration: Integrates with other AWS services for data storage, processing, and analysis.
Prometheus and Grafana
Prometheus and Grafana are used for monitoring and visualizing the performance and health of the platform.
Prometheus
- Time Series Data Collection: Collects metrics from various components.
- Alerting: Sends alerts based on predefined rules.
Grafana
- Dashboards: Visualizes metrics collected by Prometheus.
- Alerting: Sends notifications based on metrics data.
By leveraging these cloud computing technologies, Zynthetix ensures a robust, scalable, and efficient platform for synthetic data generation.