The COVID-19 pandemic has serious implications for the future of data, analytics, and machine learning.
The pandemic has disrupted machine learning, analytics, and data strategies at large companies around the world. Now’s a good time to look at what that has meant for leaders who rely on these tools, and what those leaders are doing to redeploy and regroup.
In this webinar, Jeffrey D. Camm and Thomas H. Davenport, authors of “Data Science Quarantined,” discuss the recent conversations they’ve had with data science and analytics experts. They’ll offer examples of disruption outcomes and strategies on how to capitalize on the new realities going forward.
In this webinar, you’ll learn:
- Why there has been a pivot to fast-cycle descriptive analytics.
- What the disruption has meant for machine learning models.
- Four steps for rebooting, including weighing data relevancy and ramping up model auditing.
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