Zach Perkel

Zach leads the Data Science and AI practice at Neal. In his role as director, Zach helps customers to develop strategies and models that weave data science and AI into core business practices. Since joining Neal in 2014, Zach has worked extensively in the areas of predictive modeling (supervised and unsupervised learning), analytics, and deep learning. Prior to joining Neal, Zach worked for eight years in the energy and high-tech manufacturing space spanning roles in research and development, product development, and strategy. Zach holds degrees in engineering and business from UC Berkeley and Georgia Tech.

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Why you need to monitor and update your predictive models during a crisis
When things aren't "business as usual", many predictive models require a hard look to verify performance. Here's how to update your models.
AI agent deployment: copilot, autopilot, and fully autonomous systems
AI agent deployment: Copilot, autopilot, or fully autonomous system
There are three key AI agent modes: Copilot, autopilot, or fully autonomous systems. Learn where each type of deployment is most effective.
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An executive introduction to recommendation systems
Recommendation systems can be used to increase wallet share growth through product discover and renewal. Here's how you can apply them.
IoT, DRL, and the modern tools for energy conservation
Advancements in perception, data management, algorithms, and IoT systems provide new tools for energy conservation and efficiency.
Building blocks for Autonomous Systems in manufacturing applications: Perception, Actuation, and Machine Teaching
Here are the three building blocks for Autonomous Systems in manufacturing applications: Perception, Actuation, and Machine Teaching
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Webinar: Autonomous Systems use cases for the energy industry
Oil & gas industries, utilities and other energy production or distribution companies can benefit from Autonomous Systems to improve their operations
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The 7 steps to create a successful computer vision PoC
From identifying the business goal to selecting the right cameras, these steps will help you create your next successful computer vision PoC.
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How to build a foundation for predictive reliability
Predictive reliability is a programmatic approach to maintenance that uses algorithms to detect issues. It's built with these key components.
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Top 4 things to consider when moving AI workloads to the edge
There are several advantages (and some disadvantages) in moving AI workloads to edge devices. A few tradeoffs include feasibility, cost transparency,...
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Deep learning in computer vision starts with data
Three keys to a good datase: quality, quantity, and variety. Here's how to build better datasets and enable your deep learning projects (with examples...