Machine Learning Bootcamp for Mobile Developers

Session Title

Speaker

Duration

Introduction to machine learning for mobile developers

Description: Machine Learning is very different from traditional programming in that you do not define instructions for the computer but letting it learn from examples and find out the instructions by itself. In this session, you will learn about the approach of machine learning and see some examples of how it is used in mobile applications.

Train a flower classifier with TF Lite Model Maker and integrate it to an Android app (Tutorial)

Description: In this tutorial session, we will teach you how to use TensorFlow Lite Model Maker to train a flower classification and deploy it into an Android app. It will show you the end-to-end workflow and give you hands-on experience of embedding machine learning into an app.

Leverage TF Lite pre-trained models

Description: TensorFlow Lite provides a set of pre-trained models that work out-of-the-box for some popular use cases such as object detection or sentiment analysis. In this session, we will learn how to use the models in your app 

Introduction to ML Kit and ML APIs on Android

Description: What shortcuts can you take when you are developing in Android? How should you use them with your custom models? In this talk, we will focus on tooling available to create ML-first designs, ready made APIs for defined ML tasks and how you can leverage CameraX in your ML app. 

Introduction to Cloud ML APIs and AutoML Vision Edge

Description: Even if you have terabytes of training data, it's not so easy to train the best ML model and deploy it for your business use cases. The bottleneck is ML expertise and scalable infrastructure. With Cloud AutoML from Google Cloud, you can hire an "AI data scientist" on your console, and let it build a ML model from your business data with a single click, without any coding, for various applications incl. object detection for images. In this session, we will show how AutoML Vision Edge could be used for building mobile AI applications.