Peter Krensky

Peter Krensky

Director Analyst
Peter Krensky is a Director, Analyst on the Business Analytics and Data Science team, specializing in data science and machine learning, including predictive and prescriptive analytics, citizen data science, augmented analytics, automated machine learning, DS/ML team structure and talent management.
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Tuesday, 19 May, 2020 08:30 AM|Tuesday, 19 May, 2020 09:15 AM
The Foundation of Data Science and Machine Learning: Delivering Value in the Age of AI

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do successful data science and machine learning initiatives look like?

Tuesday, 19 May, 2020 02:30 PM|Tuesday, 19 May, 2020 03:15 PM
AI Talent: Recruiting, Hiring, Organizing, Traning and Retaining

This session will cover trends and best practices around managing and developing not only data scientists, but the entire skills mix necessary to build successful data science teams: Data engineers, developers, machine learning specialists and domain experts. What are the best practices for attracting and retaining successful AI talent? What are the best options for upskilling, reskilling and education in data science and machine learning? What are the organizational principles for placing data science teams?

Wednesday, 20 May, 2020 08:15 AM|Wednesday, 20 May, 2020 08:45 AM
Data Science and Machine Learning Platform Types and Points of Differentiation

This session will detail the differences between various types of data science and machine learning platforms found in the Magic Quadrant and beyond. Data and analytics leaders much the right users with the right platform types, including notebook/coding, canvas/workflow and hybrid platforms. How do data science platforms vary and which one is right for my organization? What data science platform functionality has become commoditized in recent years? What functionality is truly differentiated and worth investing in?

Wednesday, 20 May, 2020 11:45 AM|Wednesday, 20 May, 2020 12:30 PM
Ask the Expert: What Is the Typical Roadmap for AI Adoption?

Be advised about the typical stages of AI adoption to achieve immediate and long-term goals for AI at the correct stages in your AI journey. Collaborate and set the right expectations with business stakeholders in leveraging AI for business value. Minimize risks to the organization, its customers and ecosystem.

Meet the experts face to face.