Editing AI Infrastructure and MLOps (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
The '''"death valley of ML"''' is the gap between a model that works in a notebook and one that runs in production. MLOps bridges this gap by applying software engineering rigor to ML workflows.

The ML lifecycle has distinct phases, each requiring different infrastructure:

'''Data management''': Raw data must be collected, validated, versioned, and transformed into features. Feature engineering is expensive and error-prone without systematic tooling. A feature store ensures that the same features computed at training time are served at inference time — preventing training-serving skew.

'''Experimentation''': Data scientists run hundreds of experiments varying hyperparameters, architectures, and datasets. Without experiment tracking, it's impossible to reproduce results or understand what caused improvements.

'''Training infrastructure''': Large models require distributed training across many GPUs. This involves data parallelism (split batches across GPUs), model parallelism (split model layers across GPUs), and pipeline parallelism (combine both). Frameworks like PyTorch FSDP, DeepSpeed, and Megatron-LM enable this.

'''Deployment''': A model must be packaged, versioned, and deployed to production infrastructure. Serving requirements differ radically: a real-time API needs <100ms latency; a batch processing job can run for hours.

'''Monitoring''': In production, models decay. Data drift, concept drift, and distribution shifts silently degrade performance. Without monitoring, you don't know your model is broken until users complain. MLOps monitoring tracks prediction distributions, feature drift, upstream data quality, and business outcome metrics.

The maturity of MLOps in an organization is a spectrum: from ad-hoc scripts to fully automated, continuously trained, monitored production systems.
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