Lana Zhudina
Marketing Campaign Manager, Deep Learning Institute (DLI) at NVIDIA
Fremont, California, United States
Summary
👤
Senior🎓
Top SchoolLana Zhudina is a marketing and operations leader with 8+ years building and scaling technical training programs at companies like NVIDIA and Mirantis. She designs and optimizes integrated go-to-market campaigns that drove DLI’s global adoption and previously turned Mirantis’ merged training operations from negative margins to +20% while tripling exam registrations. Comfortable at the intersection of marketing, education, and technology, she combines LMS and e-commerce expertise with hands-on campaign execution and cross-functional enablement for sales, curriculum, and customer success. Lana also contributes to open-source ML tooling—fixing PyTorch AMP, device selection, and model-loading issues in the high-profile ultralytics/YOLOv5 repo—bringing practical engineering chops to marketing for AI products. Based in Fremont, CA, she holds an MBA and MS in internet marketing and is known for operationalizing complex program changes that improve both customer satisfaction and business metrics.
8 years of coding experience
8 years of employment as a software developer
MBA, Marketing, MBA, Marketing at California State University-East BayMS, Internet Marketing, MS, Internet Marketing at University of San Francisco
English, Russian
Stackoverflow
26reputation
1kreached
3answers
0questions
Github Skills (18)
pytorch10
python10
machine-learning10
onnx10
deep-learning10
trainings10
object-detection10
modeling10
ampl9
computer-vision9
tflite7
coreml7
loss6
neural-network6
tensorflow6
Programming languages (3)
Github contributions (5)
ultralytics/yolov5
Jun 2020 - Apr 2021
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Role in this project:
ML Engineer
ML EngineerContributions:14 PRs, 39 comments, 8 issues in 10 months
Contributions summary:Lana primarily contributed to the YOLOv5 repository by addressing issues related to model loading and compatibility. They fixed bugs in the test and train scripts, specifically regarding device selection, mixed precision training, and model state loading. Additionally, the user updated the codebase to utilize PyTorch's native Automatic Mixed Precision (AMP) training feature and resolved indentation issues and refactoring/code style concerns.
pytorchdeep-learningyolov8object-detectionmachine-learning
Lornatang/RCAN-PyTorch
Feb 2022 - Dec 2022
Contributions:26 commits, 24 pushes, 1 branch in 10 months
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