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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