Neural Network Optimization with
Nick Romano of Deeplite
The cloud was a gift to AI, enabling the compute power necessary for company to leverage powerful ML algorithms. As edge technologies such as smart phones, drones, and autonomous driving begin to incorporate greater cognitive capabilities, processing speeds and decision making need to be more efficient at the device level. To solve this problem, Deeplite has emerged with a solution that optimizes neural networks.

In this episode, I sit with Nick Romano, Co-Founder and CEO of Deeplite. We discuss the challenges of deploying AI in the field, data collection advice for startups, getting an MVP off the ground, retaining senior level talent, and the greatest challenges to AI adoption.

If your company is looking to scale it’s AI initiatives, head over to Tesoro AI ( – experts in AI strategy, staff augmentation, and AI product development.
Show Notes with Timestamps (VIDEO):
1:30 What is neural network optimization? Enabling deep learning at the edge
6:14 Example for how neural network optimization is working in the field
11:15 “Biggest challenge in building AI applications?”
12:56 Data collection advice for startups
15:08 Common errors to avoid when building data driven applications
17:11 Where is AI actually being adopted?
23:00 Tackling customer acquisition starting from zero
26:00 Developing the first version of the product and solving the search for talent
32:00 Keeping senior level AI talent engaged in the day to day work at a startup
35:15 Nick’s favorite tech tool: PyTorch
35:57 Why Nick is inspired by his father’s entrepreneurial history


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