Typically, the machine learning process comprises the following steps:
- Data wrangling: This involves setting up and managing notebook environments. This will help you to get data to notebooks securely.
- Experimentation: This involves setting up and managing clusters. This will help you scale/distribute ML algorithms.
- Deployment: This involves setting up and managing inference clusters. This will help you manage and autoscale inference APIs with testing, versioning, and monitoring.
The preceding cycle can take as long as 6 to 18 months. However, given the potential of machine learning and AI to empower and improve businesses, the effort is often worthwhile. Challenges in large-scale machine learning ...
