Build smart cross-platform mobile apps - Flutter & MLKit Integration

Building “smarter” applications is a popular and important topic in the mobile development world. Machine Learning is the primary tool used to create apps that improve their delivered value by dynamically adjusting as more relevant data is made available. ML Kit and Flutter, which is Google’s mobile SDK for building native Android and iOS apps from a single codebase, can build apps with machine learning capabilities. In this session, we will explore how to use Flutter to leverage ML Kit’s functionalities and how we can train custom models using Google Cloud AutoVision API. The demo includes how to export the models to infer in the mobile app using Flutter and Firebase MLKit.

Transcript

1. Build Smart Cross-Platform Apps Flutter & ML Kit SivamuthuKumar Byteconf Flutter, August 14, 2020 Build Smart Cross-Platform Apps Flutter & ML Kit SivamuthuKumar Byteconf Flutter, August 14, 2020
2. Agenda • Flutter & Machine Learning • MLKit APIs • AutoML - Custom Model / TFLite • Flutter Integration • Demo
3. Sivamuthu Kumar Software Architect, Computer Enterprise Inc, Cloud, Mobile, IoT, ML Orlando, FL ksivamuthu ksivamuthu ksivamuthu
4. Flutter
7. Machine Learning Google’s AI Building Blocks
8. AI building blocks make it easy to add the human like capabilities of sight, language, and conversation to your applications.
9. Machine Learning APIs Pre trained models ML Engine / Deep Learning VMs Custom Models Cloud AutoML Application Developers Data scientists & Practitioners @ksivamuthu Spectrum of AI Building Blocks
10. Machine Learning APIs
11. Cloud AutoML
12. TensorFlow TPUs Google Machine Perception Pre-trained ML APIs and AutoML Energy Auto Finance Entertainment Media Manufacturing / Agriculture Retail @ksivamuthu
13. MLKit Machine Learning for Mobile Developers
14. MLKit • Optimized for Mobile – iOS / Android SDK, Flutter • Easy to use APIs – Pretrained and Custom models • On-device and Google Cloud AI Inference APIs • Fast inference time • Privacy of your data – On-Device ML Support
16. Initialize the Detectors
17. Get the image Detect / Process Image
18. Extract the Labels
19. Demo Flutter + MLKit APIs
20. Boat Classification
21. Ensure Mask
22. Ensure Mask • Preparing Dataset • Training • Evaluation • Exporting model to run in Edge/Device • Running inference in device
25. Reference • Demo Repo - https://github.com/ksivamuthu/flutter_mlkit_demo • MLKit - https://developers.google.com/ml-kit • Firebase ML Vision Plugin - https://pub.dev/packages/firebase_ml_vision • AutoML / Coral Edge Demo - https://www.youtube.com/watch?v=sZBN04tprPs
26. Thank you Sivamuthu Kumar ksivamuthu