Model training environment
Within an enterprise, a model training environment is a controlled environment with well-defined processes and policies on how it is used and who can use them. Normally, it should be an automated environment that's managed by an MLOps team, though it can be self-service enabled for direct usage by data scientists.
Automated model training and tuning are the core capabilities of the model training environment. To support a broad range of use cases, a model training environment needs to support different ML and deep learning frameworks, training patterns (such as single-node and distributed training), and hardware (different CPUs and GPUs).
The model training environment manages the life cycle of the model training process. This can include authentication and authorization, infrastructure provisioning, data movement, data preprocessing, ML library deployment, training loop management and monitoring, model persistence and registry, training job management...
