Clayton Davis
  • Bethesda, MD
  • United States
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Clayton Davis's Page

Profile Information

Short Bio:
I’m a technologist who likes to solve problems, and I’ve been blessed with a career that provides no end to the problems that need solving. At Modzy I oversee our AI work, leading a group of talented Data Scientists to build, evaluate, and deliver new capabilities into our platform. In my spare time I like to hack novel hardware (Raspberry Pi 4, I’m looking at you), plan the intelligentification of my house (code named don’t-tell-mom-the-house-can-hear-her), or pretend I’m still a Physicist while answering my kids’ questions about the universe.
Job Title:
Head of Data Science
C-Level/SVP/Executive Team
Job Function:
Data Scientist
Number of employees:
1 to 49
LinkedIn Profile:
Topics of Interest
Data Science, Machine Learning, Deep Learning, Business Analytics, Neural Networks, Mathematics

Clayton Davis's Blog

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Posted on September 7, 2021 at 10:30am 0 Comments

As your team invests significant time and resources developing models, it is imperative that processes are put into place to protect and maximize the return on that investment. To that end, in this installment of the ModelOps Blog Series we’ll discuss leveraging functionality provided by continuous integration/continuous deployment (CI/CD)…


Using Automated Builds in ModelOps

Posted on August 17, 2021 at 7:00am 0 Comments

In this installment of the ModelOps Blog Series, we will transition from what it takes to build AI models to the process of deploying into production. Think of this as the on ramp for extracting value from your AI investments—moving your model out of the lab and into an environment where it can provide new insights for your organization or add value…


Model Versioning: Reduce Friction. Create Stability. Automate.

Posted on May 12, 2021 at 7:30am 0 Comments

The research and development (R&D) phase of building an AI model to address a business problem is characterized by rapid exploration and iteration. Everything is on the table and experimentation is encouraged, from understanding how to frame the problem, to determining how to most effectively use the data on hand, to discovering the model…


Model Training: Our Favorite Tools in the Shed

Posted on April 15, 2021 at 12:17pm 0 Comments

Welcome to the second installment of our ModelOps blog series, where we dive deep into the next step in the ModelOps pipeline, Model Training. During Model Training, we feed large volumes of data to our model so it can learn to perform a certain task very well. This blog follows the first post in our series where we cover everything you need to know…


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