Building Pet Health Tech: ML, Sensors, and Dog Behavior Data – Sofya Yulpatova

In this session Sofya shares her journey building a pet-tech startup that blends machine learning sensor data and canine behavior analytics. She walks through her path from early programming explorations to launching a health monitoring device designed around anomaly detection and long-term behavioral baselines.

You’ll learn about:
– How sensor streams and sleep-cycle data enable practical canine health insights
– Why anomaly detection is more effective than classification for early issue detection in pet health
– What it takes to transition from hobby coding to a full-time engineering career
– Approaches to collecting reliable behavioral data from companion animals
– Practical realities of bootstrapping a hardware-enabled startup
– How community events and coworking spaces support co-founder discovery and product iteration
– Lessons from skipping university and focusing on hands-on experience for startup success

TIMECODES:
00:00 Sofya’s pet tech startup with machine learning sensor data and behavior pattern analytics
10:00 Journey from programming hobby to full time software development career
17:20 Career growth after skipping university and building practical experience
24:07 Puppy adoption story and family influence on pet focused innovation
32:16 Dog health monitoring framed as anomaly detection in real world machine learning
37:05 Collecting canine data with emphasis on sleep patterns and cycle tracking
43:35 Establishing a dogs normal baseline through long term data observation
49:34 Startup funding through personal savings and early stage bootstrapping
55:28 Finding cofounders and collaborators through meetups and coworking communities
59:48 Closing insights on Sofya’s educational path and early device prototypes

This talk is ideal for engineers, data scientists and founders interested in real-world ML applications, hardware startup challenges, or pet-health innovation. It also suits anyone exploring a transition into software or applied machine learning from non-traditional educational backgrounds.

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