Mobile machine learning has reached an exciting inflection point, where innovative tools and frameworks are constantly emerging to shape the future of app development. In this rapidly evolving landscape, it can be challenging for developers to stay ahead of the curve.

To help you navigate this exciting space, we've curated a collection of in-depth articles that delve into the latest mobile machine learning development tools and frameworks. From exploring the capabilities of Core ML Tools to comparing Firebase ML Kit's text recognition features on Android and iOS, these resources provide valuable insights for developers looking to leverage AI in their mobile apps.

Exploring the Power of Mobile Machine Learning

  • Using People Detection to Enforce COVID-19 Safety Measures: Learn how machine learning can be used to enforce social distancing guidelines during the pandemic.
  • Understanding and Deploying Object Detection Models: Discover the benefits and limitations of object detection models on mobile devices.
  • Exploring Use Cases of Core ML Tools: Dive into the capabilities of Apple's Core ML Tools, a Python library that converts ML models to Core ML format.

The Rise of AI in Mobile Apps

  • PyTorch Mobile: Exploring Facebook's New Mobile Machine Learning Solution: Get an inside look at Facebook's experimental mobile deployment pipeline and its implications for mobile ML.
  • Comparing Firebase ML Kit's Text Recognition on Android & iOS: Compare the text recognition features of Google's Firebase ML Kit on both Android and iOS platforms.

Edge Cases: How AI Works with IoT Devices

  • Machine Learning Models on the Edge: Mobile and IoT: Learn how machine learning works with edge devices, including mobile devices and IoT devices.

Advanced AI Techniques for Mobile Apps

  • What's New in Core ML 3: Dive into Apple's updates to their mobile machine learning framework and its implications for developers.
  • Advanced Tips for Core ML: Get expert tips on managing large-scale mobile machine learning projects.
  • Train a MobileNetV2 + SSDLite Core ML Model for Object Detection: Learn how to train a custom object detection model using the MakeML webapp.

Comparing AI Frameworks

  • Comparing Mobile Machine Learning Frameworks: In-depth analysis of Firebase ML Kit, Clarifai, Fritz AI, Skafos.ai, and Numericcal.
  • Core ML vs ML Kit: Which Mobile Machine Learning Framework Is Right for You?: A comparison of Apple's Core ML and Google's ML Kit.

Building a Foundation in AI

  • MakeML’s Automated Video Annotation Tool for Object Detection on iOS: Learn how to create custom datasets for object detection models using MakeML.
  • Build iOS-Ready Machine Learning Models Using Create ML: Get started with building custom image classification models using Apple's Create ML.
  • Neural Networks on Mobile Devices with TensorFlow Lite: A Tutorial: A practical guide on building a mobile application using TensorFlow Lite that classifies images.

AI Benchmarks and Performance

  • iOS 12 Core ML Benchmarks: Performance benchmarks for Core ML in iOS 12, on Apple's A12 Bionic Processor.
  • Benchmarking TensorFlow Mobile on Android Devices in Production: Measure the runtime speeds of TensorFlow Mobile across Android devices.

Embracing Machine Learning as a Mobile Developer

  • Embracing Machine Learning as a Mobile Developer: Learn why machine learning can be valuable for mobile developers and explore available tools and platforms.
  • Machine Learning on iOS and Android: Explore the benefits, use cases, and developer environments for machine learning on both iOS and Android.

TensorFlow Lite and Core ML

  • How TensorFlow Lite Optimizes Neural Networks for Mobile Machine Learning: Learn how TensorFlow Lite empowers mobile developers to convert and deploy machine learning models to mobile devices.
  • Train a Core ML Object Detection Model for iOS in 4 Hours — Without a Line of Code: Use MakeML to train your own mobile-ready machine learning model without writing a line of code.

AI on the Edge

  • Reverse Engineering Core ML: Learn how to reconstruct an original machine learning model from a deployed Core ML model.
  • Train and Ship a Core ML Object Detection Model for iOS in 4 Hours — Without a Line of Code: Use MakeML to train your own mobile-ready machine learning model without writing a line of code.

Conclusion

As the landscape of AI in mobile apps continues to evolve, it's essential to stay informed about the latest tools and frameworks. By exploring these resources, you'll gain valuable insights into the world of mobile machine learning and be well-equipped to unlock its potential for your own projects.