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

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

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