Infrastructure Provisioning for Machine Learning (ML) involves setting up the essential computing resources for the entire ML model lifecycle. It encompasses hardware selection, deciding between cloud or on-premises setups, resource allocation, scalability, elasticity, and data storage management. This process is crucial for the efficient and reliable performance of ML models, cost optimization, and minimizing downtime.
- Assessment and Design
- Cloud Service Selection
- Resource Allocation and Optimization
- Data Storage and Management
- Security and Compliance Audits
- Management and Monitoring