# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 2 Related Work and State-of-the-Art
While the authors gave a detailed overview of adaption algorithms and challenges during the implementation, aspects of monitoring the appearance of concept drift are picked up in work on hand. The various methods, systems, and challenges of Automated Machine Learning (AutoML) were outlined in [21]. Concerning hyperparameter optimization, meta-learning, and neural architecture search, the common foundation of AutoML frameworks is described. Subsequently, established and popular frameworks for automating ML tasks were discussed. As the automation of the various parts in an ML pipeline becomes more mature, and the framework landscape for specific problems grows, the inclusion of ML-related automation in MLOps tasks becomes more attractive. The area of AutoML was surveyed by [PERSON] et al. in [22]. A comprehensive overview of techniques for pipeline structure creation, Combined Algorithm Selection and Hyperparameter optimization (CASH) strategies, and feature generation is discussed by the authors. As the AutoML paradigm has the potential of performance loss while training model candidates, different patterns of improving the utilization of such systems are introduced. By discussing the shortcomings and challenges of AutoML, a broad overview of this promising discipline was given by the authors. Although we refer to specific aspects of ML automation in work on hand, models' deployment and integration into the target application are treated more intensely. Concerning research data sets, [PERSON] et al. provided international guidelines on sharing and reusing quality information in [23]. Based on the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, different data lifecycle stages and quality dimensions help in systematically processing and organizing data sets, their quality information, respectively. While determining and monitoring the quality of datasets and processed data structures are vital to different operations in MLOps, the work on hand does not address the sharing of preprocessed records. A systematic approach for utilizing MLOps principles and techniques was proposed by [PERSON] in [24]. With a focus on monitoring ML model quality, end-to-end traceability, and continuous integration and delivery, a holistic overview of tasks in MLOps projects is given, and real-world projects are introduced. As the authors focused on practical tips for managing and implementing MLOps projects using the technologies Azure in combination with MLflow, the work on hand considers a broader selection of supportive frameworks and tools. Considering automation in MLOps, various roles and actors' main tasks are supported by interconnected tooling for the dedicated phases. [PERSON] et al. surveyed the degree of automation required by various actors (e.g., 239 employees of an international organization) in defining a human-centric AutoML framework in [25]. With visualizing the survey answers, an overview of the different actor's thoughts on automating the various phases of end-to-end ML life cycles was given and underlined the author's assumption of only partly automating processes in such complex and error-prone projects. Additionally, the landscape of MLOps tools has evolved massively in the last few years. There has been an emergence of high-quality software solutions in terms of both open-source and commercial options. The commercial platforms and tools available in the MLOps landscape make ML systems development more manageable. One such example is the AWS MLOps framework [26]. The framework is one of the easiest ways to get started with MLOps. The framework is built on two primary components, i.e., first, the orchestrator, and second, the AWS CodePipeline instance. It is an extendable framework that can initialize a preconfigured pipeline through a simple API call. Users are notified by email about the status of the pipeline. There are certain disadvantages of using commercial platforms for MLOps.