AI systems cannot exist without data. Now that AI models (data science and AI) have matured and are readily available to apply in practice, most organizations struggle with the data infrastructure to do so. There is a growing need for data engineers that know how to prepare data for AI systems or that can setup enterprise-wide data architectures for analytical projects. But until now, the data engineering part of AI engineering has not been getting much attention, in favor of discussing the modeling part. In this paper we aim to change this by perform a mapping study on data engineering for AI systems, i.e., AI data engineering. We found 25 relevant papers between January 2019 and June 2023, explaining AI data engineering activities. We identify which life cycle phases are covered, which technical solutions or architectures are proposed and which lessons learned are presented. We end by an overall discussion of the papers with implications for practitioners and researchers. This paper creates an overview of the body of knowledge on data engineering for AI. This overview is useful for practitioners to identify solutions and best practices as well as for researchers to identify gaps.

DataOps will soon become integral to data engineering, influencing the future of data. Many organizations today still struggle to harness data and analytics to gain actionable insights. By centering DataOps in their processes, data engineers will lead businesses to success, building the infrastructure required for automation, agility and better decision-making. DataOps is a set of practices and technologies that operationalizes data management to deliver continuous data for modern analytics in the face of constant change. DataOps streamlines processes and automatically organizes what would otherwise be chaotic data sets, continuously yielding demonstrable value to the business.

It is often said that in this business environment, every organization has to be data-driven. This translates to the fact that most organizations have to make use of data to take business decisions across the enterprise, and this is not just confined to CxO only. Data is the core of digital transformation, and is pivotal in terms of improving a user's experience. We also hear about how data, which is being considered as the'new oil,' is fueling the economic growth of 21st century. In fact, there are many Billion dollar valuation companies that have been built on Data Foundation.

In todays' modern scenario, we see more and more companies embracing and truly unlocking the power of Data Science. Most businesses would benefit from structuring their data, and the application of Data Science can be used in various fields. In the future, we will going to have even more powerful applications and will have streamlined the process, which enables smaller businesses and individuals to take advantage of data and processes like Deep Learning and Machine Learning which are all a part of Data Science. Data Science is simply nothing but a quantitative approach to a problem. Previously, due to lack of data or processing power, we relied on other things like an authoritarian whim, consensus, and expert intuition.

Artificial Intelligence represents a set of studies and techniques, typical of information technology but with significant philosophical and social implications, which has as its purpose the realization of programs and technological systems capable of solving problems and carrying out tasks normally attributable to the mind and human capabilities. Given recent progress, it is possible to identify Artificial Intelligence as the discipline that deals with creating machines (hardware and software) capable of operating autonomously. The growing attention created in this discipline is motivated by the results that can be achieved thanks to the technological maturity achieved, both in the computational calculation and in the ability to analyze in real-time and in a short time of huge amounts of data in any form [Big Data Analytics]. AI is a popular branch of computer science that concerns building "intelligent" smart machines capable of performing intelligent tasks. With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.

Artificial Intelligence represents a set of studies and techniques, typical of information technology but with significant philosophical and social implications, which has as its purpose the realization of programs and technological systems capable of solving problems and carrying out tasks normally attributable to the mind and human capabilities. Given recent progress, it is possible to identify Artificial Intelligence as the discipline that deals with creating machines (hardware and software) capable of operating autonomously. The growing attention created in this discipline is motivated by the results that can be achieved thanks to the technological maturity achieved, both in the computational calculation and in the ability to analyze in real-time and in a short time of huge amounts of data in any form [Big Data Analytics]. AI is a popular branch of computer science that concerns building "intelligent" smart machines capable of performing intelligent tasks. With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.

Dr. Francois Laborie is President of Cognite, supporting the full-scale digital transformation of asset-heavy industries in North America. Artificial intelligence has changed our lives as consumers. Why hasn't it changed our industries? AI in industry requires more than just big data to work, and the solution lies in the world of physics. If a predictive algorithm fails in the consumer industry, it's not the end of the world. Maybe an ad doesn't get clicked or a TV show doesn't get watched.

For chief data officers and data scientists, the business case for DataOps can be obvious. DataOps, correctly done, can streamline data workflows, reduce errors, and offers transparency to the entire data operations. It improves efficiency, increases data trust, and gives more time to do analysis. For business executives, such benefits are not immediately apparent. So, getting the budget to build your DataOps can run into snags -- right up until a business problem challenges your company's core value proposition. That's what happened for Screenrights.

Enterprises have struggled to collaborate well around their data, which hinders their ability to adopt transformative applications like AI. The evolution of DataOps could fix that problem. The term DataOps emerged seven years ago to refer to best practices for getting proper analytics, and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just as the DevOps trend led to a better process for collaboration between developers and operations teams, DataOps refers to closer collaboration between various teams handling data and operations teams deploying data into applications. Getting DataOps right is a significant challenge because of the multiple stakeholders and processes involved in the data lifecycle.

Self-driving cars, lifelike robots, and autonomous delivery drones are the sexy, headline-grabbing face of the digital transformation that we see all around us today. None of these would be possible, though, without data – the oil of the fourth industrial revolution – and the analytic technology we've built to allow us to interpret and understand it. Big Data is a term that's come to be used to describe the technology and practice of working with data that's not only large in volume but also fast and comes in many different forms. For every Elon Musk with a self-driving car to sell, or Jeff Bezos with a cashier-less convenience store, there is a sophisticated Big Data operation and an army of clever data scientists who've turned a vision into reality. The term Big Data itself may not be as ubiquitous as it was a few years ago, and that's purely because many of the concepts it embodies have been thoroughly embedded into the world around us.