What was the infrastructure configuration?

The infrastructure configuration in machine learning projects typically includes several key components:

• Dedicated computing resources: GPU or TPU for accelerating model training, CPU for data preprocessing.
• Data storage: distributed file systems or cloud storage (e.g., AWS S3, Google Cloud Storage) for storing large datasets.
• Development and orchestration environment: using containerization (Docker), cluster management systems (Kubernetes) for scalable experiment deployment.
• Data processing pipelines: tools for ETL (e.g., Apache Airflow) to automate data preparation.
• Monitoring and logging systems: to track model performance and infrastructure status.

An example of a simplified configuration:

- Compute: 4 x NVIDIA Tesla V100 GPUs
- Storage: 100 TB AWS S3
- Orchestration: Kubernetes cluster with 10 nodes
- Data pipeline: Apache Airflow
- Monitoring: Prometheus + Grafana

Depending on the task and budget, the configuration may vary.