AMLM2

AMLM2 is a term used in machine learning discussions to denote a second-generation automated machine learning framework or platform. There is no universally accepted definition, and the term is applied differently across sources. In general, AMLM2 signals an evolution beyond first-generation AutoML by emphasizing broader lifecycle automation, reproducibility, and governance.

Core ideas attributed to AMLM2 include automated data ingestion and preprocessing, feature engineering, model selection, and hyperparameter optimization, followed by automated model training, evaluation, and deployment. Proponents also highlight built-in monitoring, drift detection, and automated retraining as part of the lifecycle. A distinguishing feature may be standardized interfaces and interoperability with existing ML libraries and operational platforms to support end-to-end pipelines from data to production.

Architecturally, AMLM2 concepts often describe a modular system with a pipeline manager or orchestrator, a model repository or zoo, and a benchmarking suite that compares candidate models under consistent evaluation criteria. Reproducibility, traceability, and governance are emphasized, with versioning for data, features, and models, and audit trails for decisions made during automation and deployment. Privacy, security, and compliance considerations are frequently incorporated into the design.

Usage of AMLM2 terminology ranges from research labels for AutoML improvements to product marketing. It is not tied to a single standard and may refer to different implementations that meet similar goals such as reducing manual effort, improving model quality, and accelerating deployment.