Machine Learning for Mobile Apps using Google’s ML Kit Training Course

Course Code

mlkit

Duration

14 hours (usually 2 days including breaks)

Requirements

  • An understanding of machine learning
  • Experience with mobile development

Audience

  • Developers

Overview

ML Kit is a mobile SDK provided by Google for integrating machine learning technologies into Android and iOS apps. It features usable APIs for barcode scanning, face detection, image labeling, object detection and tracking, text recognition, translations, and other vision and language processing functions.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimized for processing on mobile devices.

By the end of this training, participants will be able to:

  • Set up the necessary development environment to start developing machine learning features for mobile apps.
  • Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
  • Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

  • ML Kit vs TensorFlow vs other machine learning services
  • Overview of ML Kit features and components

Getting Started

  • Setting up the ML Kit SDK
  • Exploring APIs and sample apps

Implementing ML Kit Vision APIs

  • Automating data entry (Text Recognition)
  • Detecting faces for selfies and portraits (Face Detection)
  • Interpreting body positions (Pose Detection)
  • Adding background effects (Selfie Segmentation)
  • Integrating Barcode Scanning
  • Identifying objects, places, species, etc. (Image Labeling)
  • Locating prominent objects in an image (Object Detection and Tracking)
  • Recognizing handwritten texts (Digital Ink Recognition)

Working with Natural Language APIs

  • Identifying languages
  • Translating texts
  • Generating smart replies
  • Using entity extraction

Optimizing Existing Apps with ML Kit

  • Using custom models with ML Kit
  • Migrating from Firebase to the new ML Kit SDK
  • Migrating from Mobile Vision to ML Kit SDK
  • Reducing app size for deployment
  • Refactoring apps to use dynamic feature modules

Troubleshooting Tips

Summary and Next Steps

Testimonials

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