The use of artificial intelligence in medicine is growing rapidly.Credit: ktsimage/Getty Each day, around 350 people in the United States die from lung cancer. Many of those deaths could be prevented by screening with low-dose computed tomography (CT) scans. But scanning millions of people would produce millions of images, and there aren't enough radiologists to do the work. Even if there were, specialists regularly disagree about whether images show cancer or not. The 2017 Kaggle Data Science Bowl set out to test whether machine-learning algorithms could fill the gap.

The Indian Institute of Science (IISc), Bangalore is organizing an Online Workshop on AI & IoT for Flow Modeling on November 20, 2020, from 11 am to 2 pm. The Indian Institute of Science (IISc) is a public, deemed, research university for higher education and research in science, engineering, design, and management. It is located in Bangalore (Bengaluru), in the Indian state of Karnataka. The workshop is organized as part of the Indo-Dutch project, "Digital Twins for pipeline transport networks". The aim of the project is to develop a digital twin that connects sensor data and advanced fluid solvers in order to detect possible leakage of fluid from the pipeline in real-time.

The goal of data science teams are to build and deploy high impact models. Data scientists prefer to focus on building algorithms, while data engineers focus on performance and productionizing machine learning. Kubernetes is an orchestration platform that can be deployed anywhere and can serve any kind of machine and deep learning environment. Kubernetes is a great tool for data scientists to use to stay productive and for data engineers to get production-ready results. In this free workshop you'll learn how to build your own Kubernetes to use in your next machine learning pipeline.

Machine Learning is cognitive computing process that integrates artificial intelligence with our every day machines. As the term suggests, the concept of Machine Learning creates a continuous learning procedure for the machine itself. With Machine Learning, the most familiar of machines, our everyday computers can now learn on their own and make decisions. This leads to lesser human interaction and interference along with decreased amount of programming. The idea is to program the computer in a manner to allow it to program itself in the future with smart and implicit decisions.