The effectiveness of AI efforts can be quantifiably increased using tried-and-true MLops methodologies in terms of time to market, results, and long-term sustainability. The long-term success of AI projects depends on effectively closing that operational capability gap because building models that make accurate predictions are only a small portion of the entire task. There is more to creating ML systems that add value to a company. An efficient technique calls for regular iteration cycles with ongoing monitoring, care, and improvement, as opposed to the ship-and-forget pattern typical of traditional software. Enter MLops (machine learning operations), which enables teams from the IT operations, engineering, and data science departments to collaborate to deploy ML models into production, manage them at scale, and continuously track their performance. MLops typically aims to address six critical challenges around taking AI applications into production.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) adoption keeps growing. According to a McKinsey survey, 56% of companies are now using AI in at least one function, up from 50% in 2020. A PwC survey found that the pandemic accelerated AI uptake and that 86% of companies say AI is becoming a mainstream technology in their company. In the last few years, significant advances in open-source AI, such as the groundbreaking TensorFlow framework, have opened AI up to a broad audience and made the technology more accessible.

Robustness and its many related concepts (stability, resilience, reliability, sustainability, etc.) are essential to understanding and maintaining systems of all kinds: engineered systems, ecologies, political regimes, computer algorithms, economies, homeostatic organisms, and decision procedures to name a few. However the concepts in this family have not been generally and formally defined and, as a result, the terms' uses across these various applications are inconsistent and sometimes contradictory. As part of a larger research project encompassing several categories of dynamical properties this paper distinguishes among several different robustness-related concepts using formal and general definitions of each. In addition to providing conceptual clarity through rigorous mathematical definitions, the techniques can also be used as domain-agnostic measures of the included properties. To help realize the potential of complex systems models we need such measures to capture features of processes that exhibit feedback, nonlinearity, heterogeneity, and emergence. The paper finishes with several branches of future work involving applications of these measures, more new measures for complex systems, and establishing of equivalence classes for the dynamics of complex systems for behavior-based categorization.