AutoML Vision and Edge TPU: Bringing TensorFlow Lite models to edge devices
Who is this presentation for?
- Data scientists, ML engineers, mobile developers, and embedding systems engineers
AutoML Vision from Google Cloud automates the process of vision recognition model training with Google’s state-of-the-art technologies such as meta learning (learning2learn) and neural architecture search (NAS) technology combined with transfer learning, data augmentation, and so on. You can just upload the training data with labels, press a train button, and wait for 24 hours to get a ML model with the cutting-edge accuracy. AutoML Vision now supports exporting of TensorFlow Lite model for prediction on smartphones, small devices (such as Rasberry Pi), and Edge TPU, the hardware accelerator developed by Google. So you can bring the power of AutoML to those edge devices and achieve significantly shorter latency on image classification.
Kaz Sato shows you how the whole process works. Without any ML expertise, you can easily train the image recognition model on the cloud, export the TensorFlow Lite model, and use it at small devices with short latency and low power consumption.
- A basic understanding of TensorFlow
What you'll learn
- Learn about AutoML Vision, which now supports model export for mobile phones, edge devices, and Edge TPU
Kaz Sato is a staff developer advocate on the cloud platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He’s a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata + Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.
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