With the rise of edge computing, various AI services are expected to be available at a mobile side through the inference based on deep neural network (DNN) operated at the network edge, called edge inference (EI). On the other hand, the resulting AI quality (e.g., mean average precision in objective detection) has been regarded as a given factor, and AI quality control has yet to be explored despite its importance in addressing the diverse demands of different users. This work aims at tackling the issue by proposing a feature hierarchical EI (FHEI), comprising feature network and inference network deployed at an edge server and corresponding mobile, respectively. Specifically, feature network is designed based on feature hierarchy, a one-directional feature dependency with a different scale. A higher scale feature requires more computation and communication loads while it provides a better AI quality. The tradeoff enables FHEI to control AI quality gradually w.r.t. communication and computation loads, leading to deriving a near-to-optimal solution to maximize multi-user AI quality under the constraints of uplink \& downlink transmissions and edge server and mobile computation capabilities. It is verified by extensive simulations that the proposed joint communication-and-computation control on FHEI architecture always outperforms several benchmarks by differentiating each user's AI quality depending on the communication and computation conditions.

Over the past few years, inspired by the promise of Artificial Intelligence (AI), we have seen enterprises embrace the first big challenge of AI: building it in the first place. There has been significant adoption of machine learning (ML) and AI in enterprises, aided by the broad availability of solutions for data preparation, model development and training, and model deployment. Now, however, we are seeing enterprises shift their focus from getting these basic building blocks in place to tackling the next big challenge: how do you drive real, sustainable business value with AI? Answering this question requires solving a whole new set of problems. It requires solving the challenge of AI Quality. At TruEra, we believe that solving the problem of AI Quality is key to driving and preserving business value.

Are you a data scientist or machine learning engineer interested in learning more about how to analyze and improve the performance and trustworthiness of your machine learning models? Then this live online course is for you! AI Quality: Driving ML Performance and Trustworthiness is a free course taught live by five experts from leading universities, including a professor from Carnegie Mellon University and Stanford University. This offer is exclusively for corporate and government practitioners. All students completing the course receive a certificate, limited edition shirt, and access to the Slack community.

"Quality is never an accident. It is always the result of intelligent effort" – John Ruskin The adoption of artificial intelligence (AI) is gathering pace. And with a significant level of adoption in emerging markets, the trend has seen an increase in almost every industry, encompassing a range of business sectors from production, through marketing and sales to HR and risk management. Alongside this trend, companies are broadening their focus to include stakeholders beyond their shareholders. This can be attributed to a variety of factors.