MLOps Is Overfitting. Here’s Why
VC surveys show there are hundreds of companies active today that define themselves as being part of the MLOps category.
MLOps systems provide the infrastructure that allows ML practitioners to manage the lifecycle of their work from development to production in a robust and reproducible manner. MLOps tools may cover E2E needs or focus on a specific phase (e.g., Research/Development) or artifact (e.g., Features) in the process.
The world of data involves a continuum of data practitioners, from analysts using mainly ad hoc SQL statements to PhDs running proprietary algorithms.
Is there one DevOps approach to rule them all, or is ML a unique practice that requires its own approach for best practices and a matching infrastructure?