The increasing usage of machine learning (ML) coupled with the software architectural challenges of the modern era has given rise to two broad research challenges: i) Software architecture (SA) for ML-based systems and ii) ML techniques for (better) architecting any software system. In recent times, both the research and practitioner community have started exploring these research areas at the intersection of SA and ML. As a result, there have been emerging contributions from the scientific and practitioner community in these two research areas. However, these contributions are scattered across different communities of software engineering, self-adaptation, ML, etc. The goal of SAML 2022 is to bring together practitioners and the research community in one common platform to explore: i) how to come up with better SA practices for architecting ML-based systems; ii) how to leverage ML techniques to better architect software systems; iii) state of research and practice in architecting ML-based systems and in using ML techniques for architecting modern software systems. Further, SAML 2022 shall also provide a common forum to bring together both practitioners and researchers of SA and ML communities to identify and fill the research gaps that can benefit both communities. SAML 2022 seeks contributions in the form of full research papers, industry experience reports and short papers in topics including but not limited to:

Architecture design, analysis, and evaluation of ML-based systems
Architecture frameworks, patterns, and models for ML-based systems
Integration of the ML development and software development processes
ML system relevant quality attributes and their analysis
Using AI/ML to synthesize or analyze architecture documentation
Using AI/ML for the detection of architecture and design (anti-)patterns
Using AI/ML to guide or conduct architectural refactorings
Using AI/ML for architecture evaluation
Architecting self-adaptive systems using AI/ML
Role of software architect in architecting ML-based systems
Software Architecture and MLOps practices
Role of the software architect in architecting ML-based systems
Quality assurance of ML-based systems
Architecture and technical debt in ML-based systems
Maintenance and evolution of ML-based systems
Case studies and experience reports
Social and organizational aspects of architecting ML-based systems
Architecting data or ML pipelines