Everything is connected and the clock is ticking: AI and big ag data for food security

Nick Curcuru, VP, Data Analytics and Cyber Security, will discuss Mastercards commitment to AI and its recent investments and developments.

In recent years, we've seen tremendous improvements in artificial intelligence, due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.

Dean Wampler dives into how (and why) to integrate ML into production streaming data pipelines and to serve results quickly; how to bridge data science and production environments with different tools, techniques, and requirements; how to build reliable and scalable long-running services; and how to update ML models without downtime.

Machine learning and artificial intelligence are no longer science fiction, so now you have to address what it takes to harness their potential effectively, responsibly, and reliably. Based on lessons learned at Google, Cassie Kozyrkov offers actionable advice to help you find opportunities to take advantage of machine learning, navigate the AI era, and stay safe as you innovate.

Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoMLtranslating from the hype into business concerns and practices in a vendor-neutral way.

Modeling is easyproductizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karaua system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes.