From Video Camera Feed to AI Insights: A Complete Pipeline Using LandingLens And Snowflake

Описание: Are you curious about how to turn a live camera feed into actionable insights in real time without writing a ton of code or setting up a complex infrastructure?

In this hands-on demo, Andrea Kropp, Machine Learning Engineer at LandingAI, walks through the process of using Snowflake and LandingLens, a Snowflake Native vision AI app, to connect to a YouTube livestream, detect vehicles, such as passenger cars, trucks, and school buses, and visualize the data in a real-time dashboard built with Streamlit. No GPU setup required. She also shows how this technology can be used for such real-world applications as monitoring doggy daycares, city signage in Thailand, and even wildlife in the Namib Desert.

You’ll learn how to:
--Collect and label training images from a video stream
--Train a custom object-detection model using LandingLens
--Deploy the model to an API endpoint
--Send images from a live feed for inference every 10 seconds
--Store structured results in Snowflake tables
--Build interactive analytics and visualizations in Streamlit
--Expand this setup to other use cases, from traffic monitoring to wildlife detection

This video is ideal for:
--Data scientists and ML engineers
--Snowflake users exploring native apps
--Developers and solution architects
--City planners, transportation analysts, and anyone interested in video analytics
-Anyone curious about bringing computer vision into a no-code/low-code workflow

Check out LandingAI's Snowflake Native App here:
👉 https://app.snowflake.com/marketplace...

Learn more about LandingLens and try out some sample code using the same livestream feed by visiting https://landing.ai/snowflake

Got questions or ideas? Drop them in the comments. We'd love to hear how you're using computer vision in your world.

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