In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in current industrial practice and its expected relevance in the future. The interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. This enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. Their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models with existing methods and frameworks.

Google AlphaGos win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education). These advances have resulted in numerous new concepts and technologies, which are too many for people to catch up to and even make them confused, especially for newcomers to the ML area. This paper is aimed to present a clear picture of the state-of-the-art of the existing ML technologies with a comprehensive survey. We lay out this survey by viewing ML as a MLOps (ML Operations) process, where the key concepts and activities are collected and elaborated with representative works and surveys. We hope that this paper can serve as a quick reference manual (a survey of surveys) for newcomers (e.g., researchers, practitioners) of ML to get an overview of the MLOps process, as well as a good understanding of the key technologies used in each step of the ML process, and know where to find more details.

Machine Learning life cycle or Machine Learning Development Life Cycle to be precise can be said as a set of guidelines which need to be followed when we build machine learning based projects. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project. A beginner always think that making a machine learning project is just about finding accuracy of model or performing EDA(Exploratory Data Analysis).But when it comes to real life product there are lot of stuff which needs to be consider to make a end-to-end applications.So lets see what are various steps involved in making real world machine learning projects. This is the basic as well as an essential step in developing machine learning project.

This blog mainly tells the story of the Machine Learning life-cycle, starting with a business problem to finding the solution and deploying the model. This helps beginners and mid-level practitioners to connect the dots and build an end-to-end ML model. Here are the steps involved in an ML model lifecycle. All the above-mentioned steps are explained in simple words below. Telecom customer Churn's use case is taken as an example throughout the blog to keep things simple.

The life cycle of a machine learning project is complex. In the paper Hidden Technical Debt in Machine Learning Systems, Google took the reference of the software engineering framework of technical debt and explained that the maintenance of real-world ML systems can incur massive costs. The below image truly depicts the real scenario. The sandwiched tiny black box, surrounded by big boxes is the Magic Machine learning Code:) and to run this magic code in the production, we need to deal with several other processes e.g. Apart from that when the ML system is in the exploration phase, a team of data scientists/ML engineers keep close eyes on the metrics and performance of the different models to get an optimized one.