Schedule: Executive Briefing/Best Practices sessions

While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle. Read more.

Amid fears of sentient killing robots and a freezing AI winter, AI has a true potential to transform the enterprise. Actualizing this potential requires a well-informed organizational strategy and consistent execution of best practices regarding people, processes, and platforms. Bahman Bahmani examines these strategies and best practices and provides insights into their successful execution. Read more.

The Analytics Impact Index gives organizations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Anastasia Kouvela and Bharath Thota walk you through the 2019 results and the analytics journey of leading global organizations and empower companies to develop a case for change. Read more.

As robots and AI systems become more prevalent in enterprise, industrial, and home settings, there's an increasing need for well-maintained, reliable, and secure development tools and frameworks for the next-generation production-grade robots and systems. Cam Buscaron explains how to leverage large-scale cloud simulation and the Robot Operating System (ROS) to build such systems. Read more.

In every AI initiative, there’s a demand from businesses to protect or increase market share or decrease operational costs. Your competitors are a growing threat, seemingly adopting new technologies better than you. Umit Cakmak examines critical steps from countless client engagements on how to consistently deliver successful AI projects. Read more.

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 AutoML—translating from the hype into business concerns and practices in a vendor-neutral way. Read more.

The growing complexity of data science leads to black box solutions that few people in an organization understand. Mark Madsen explains why reproducibility—the ability to get the same results given the same information—is a key element to build trust and grow data science use. And one of the foundational elements of reproducibility (and successful ML projects) is data management. Read more.