ACM Queue

The Secret Formula for Choosing the Right Next Role: The best careers are not defined by titles or resume bullet points. - Kate Matsudaira... Changing jobsespecially the higher up you get in your careeris a complex process. There are so many factors to consider, and often the factors that stand out most are the ones that matter the least: fancy titles, exciting projects, tempting promises of future success But those factors that seem so valuable in the moment are just thatthey are momentary. Your career isn't just about this one next step you're taking. Your career is a journey that will last a long time. It is smarter to invest in your long-term success. Focus on factors that will increase your career capital and make you a more valuable hire in your next role, and the one after that, and the one after that. When you are looking at the options for your next role, there are smarter choices that you can make. Here are the most important factors to consider when picking your next opportunity. http://ow.ly/VG0p30l8d0N

The Mythos of Model Interpretability In machine learning, the concept of interpretability is both important and slippery. - Zachary C. Lipton... Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? Models should be not only good, but also interpretable, yet the task of interpretation appears underspecified. The academic literature has provided diverse and sometimes non-overlapping motivations for interpretability and has offered myriad techniques for rendering interpretable models. Despite this ambiguity, many authors proclaim their models to be interpretable axiomatically, absent further argument. Problematically, it is not clear what common properties unite these techniques. This article seeks to refine the discourse on interpretability. First it examines the objectives of previous papers addressing interpretability, finding them to be diverse and occasionally discordant. Then, it explores model properties and techniques thought to confer interpretability, identifying transparency to humans and post hoc explanations as competing concepts. Throughout, the feasibility and desirability of different notions of interpretability are discussed. The article questions the oft-made assertions that linear models are interpretable and that deep neural networks are not. http://ow.ly/yjT030l0BGe

Everything Sysadmin GitOps: A Path to More Self-service IT (IaC + PR = GitOps)... - Thomas A. Limoncelli GitOps lowers the cost of creating self-service IT systems, enabling self-service operations where previously they could not be justified. It improves the ability to operate the system safely, permitting regular users to make big changes. Safety improves as more tests are added. Security audits become easier as every change is tracked. http://ow.ly/Djtf30kUdu4

Mind Your State for Your State of Mind: The interactions between storage and applications can be complex and subtle. - Pat Helland... Applications have had an interesting evolution as they have moved into the distributed and scalable world. Similarly, storage and its cousin databases have changed side by side with applications. Many times, the semantics, performance, and failure models of storage and applications do a subtle dance as they change in support of changing business requirements and environmental challenges. Adding scale to the mix has really stirred things up. This article looks at some of these issues and their impact on systems. http://ow.ly/ud3530kOSth

Research for Practice: FPGAs in Data Centers This installment of Research for Practice features a curated selection from Gustavo Alonso, who provides an overview of recent developments utilizing FPGAs (field-programmable gate arrays) in datacenters. As Moore's Law has slowed and the computational overheads of datacenter workloads such as model serving and data processing have continued to rise, FPGAs offer an increasingly attractive point in the trade-off between power and performance. Gustavo's selections highlight early successes and practical deployment considerations that inform the ongoing, high-stakes debate about the future of datacenter- and cloud-based computation substrates. http://ow.ly/VV3u30ko4YN