Google's Cloud Machine Learning Engine enables let's you create a prediction service for your TensorFlow model without any ops work. Get more time to work with your data, by going from a trained model to a deployed, auto-scaling prediction service in a matter minutes. So, we've gathered our data, and finally finished training up a suitable model and validating that it performs well. We are now finally ready to move to the final phase: serving your predictions. When taking on the challenge of serving predictions, we would ideally want to deploy a model that is purpose-built for serving.
You've heard the term, and you probably nod in agreement when someone tells you how important it is. But secretly you may not be sure what it is or how it works. Ask your data scientists to explain, and you may get lost in a sea of specialist talk about forks, leaf nodes, split points, and recursions. The only thing you need to know is that machine learning applies statistical models to the data you have in order to make smart predictions about data you don't have. Those predictions can help you find signals in the noise and extract value from all the data you're collecting.
Acute Kidney Injury (AKI) is a common occurrence for critically ill patients in the ICU, and its early diagnosis has proven to be challenging. The accuracy of the online, machine-learning-based prediction model, AKIpredictor, was analysed for its use in a clinical setting. The study, which took place over five ICUs in Belgium, compared the predictions of AKIpredictor with physician predictions. The patient information for 250 individuals with no prior evidence of AKI or end-stage renal disease before ICU admission was used. Physicians then predicted AKI progression at three stages: at the initial admission, on the patient's first morning in the ICU and 24 hours later.
Last week, I started a blog series, looking at some of the loftiest technology predictions for 2020 and beyond, and how it will impact construction specifically. Last week's topic was the IoT (Internet of Things), and this week I would like to dive into AI (artificial intelligence) and ML (machine learning). Now, before we look at predictions for where the market is headed, let's take a quick minute to define these terms. Too often, people use the acronyms, IoT, AI, and ML interchangeably, but they are in fact very different. Quite simply, AI is the data or the intelligence that tells a machine what to do, while ML studies previous datasets and makes predictions for future datasets.
In this work we study the problem of using machine-learned predictions to improve performance of online algorithms. We consider two classical problems, ski rental and non-clairvoyant job scheduling, and obtain new online algorithms that use predictions to make their decisions. These algorithms are oblivious to the performance of the predictor, improve with better predictions, but do not degrade much if the predictions are poor. Papers published at the Neural Information Processing Systems Conference.