Presentation: Building a Security System with Image Recognition & an Amazon DeepLens

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 4:00pm - 4:10pm

Day of week: Tuesday

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Abstract

This quick talk will show you step by step how I build a security system for my house using the upcoming Amazon DeepLens. I'll go over how I built and trained the models, and the steps necessary to get the camera making inferences and sending alerts.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Jeremy Edberg

CEO and Founder @MinOpsInc

Jeremy is an angel investor and advisor for various incubators and startups, and the founder of MinOps. He was the founding Reliability Engineer for Netflix and before that he ran ops for reddit as it's first engineering hire. Jeremy also tech-edited the highly acclaimed AWS for Dummies. He is a noted speaker in serverless computing, distributed computing, availability, rapid scaling, and cloud computing, and holds a Cognitive Science degree from UC Berkeley.

Find Jeremy Edberg at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.