Abstract

International audience; The Machine Learning (ML) world is in constant evolution, as the amount of different algorithms in this context is evolving quickly. Until now, it is the responsibility of data scientists to create ad-hoc ML pipelines for each situation they encounter, gaining knowledge about the adequacy between their context and the chosen pipeline. Considering that it is not possible at a human scale to analyze the exponential number of potential pipelines, picking the right pipeline that combines the proper preprocessing and algorithms is a hard task that requires knowledge and experience. In front of the complexity of building a right ML pipeline, algorithm portfolios aim to drive algorithm selection, learning from the past in a continuous process. However, building a portfolio requires that (i) data scientists develop and test pipelines and (ii) portfolio maintainers ensure the quality of the portfolio and enrich it. The firsts are the developers, while the seconds are the operators. In this paper, we present a set of criteria to be respected, and propose a pipeline-based meta-model, to support a DevOps approach in the context of Machine Learning Pipelines. The exploitation of this meta-model, both as a graph and as a logical expression, serves to ensure continuity between Dev and Ops. We depict our proposition through the simplified study of two primary use cases, one with developer's point-of-view, the other with ops'.

Abstract

One of the main topics in IT Portfolio Management, according to IT Governance models, is to plan and control information systems that are aligned with the company mission and objectives. The goal of the IT Portfolio Planning is to define which information systems are necessary and with which priority. In general, planning methodologies trace directions from a present point to a future and desired target. However, in many cases, companies can not control situations; events may occur that can not be avoided. A scenarios-based planning methodology can help managers to identify future events, their probability and consequences. A scenario represents a future situation that can not be controlled nor can be avoided. However, the study of future scenarios can help managers to plan reactions, so that company can create mechanisms for avoiding problems or for minimizing bad consequences. The goal of this chapter is to present an adaptation of the scenario-based methodology for IT and Information Systems Planning. The chapter will describe in details each step of the proposed methodology and discuss a study case. Steps include the identification of different scenarios and their corresponding antecedent events, the determination of probabilities and consequences of the events, how to calculate risks and how to plan Information Systems and IT resources to manage each scenario. Document type: Part of book or chapter of book