Firebase ML Kit 101 : Introduction

image courtesy: https://firebase.google.com/products/ml-kit/

Firebase ML Kit was introduced to us at Google I/O ’18. It is a mobile SDK that enables Android and iOS app developers to have advanced machine learning capabilities into their apps with ease.

Nowadays, machine learning has become an integral part of mobile development. Big companies like Uber, Facebook, Microsoft etc. rely heavily on machine learning for their businesses. It helps them to know their users better and provide them with a better experience on their apps.

So, as a mobile developer, it is important for us to integrate some kind of intelligence into our apps for a better user experience.

There’s no need to have any ML knowledge to get started!

ML Kit comes with a set of ready to use APIs for common use cases and it just takes a few lines of code to implement these APIs into your apps.

If the ML Kit doesn’t have an API that suits your use case then ML Kit also provides convenient APIs that help you use your custom TensorFlow Lite models in your mobile apps.

ML Kit APIs works both on the device and on the cloud. The on-device APIs are designed to work fast with no internet connection. On the other hand, cloud-based APIs uses Google Cloud Platform’s machine learning technology which gives more accurate results but requires an internet connection.

ML Kit features available on the device and/or in the cloud

Note: Firebase ML Kit is in beta as of October ‘18.

Firebase ML Kit Series

In these series of articles, we will deep dive into different ML Kit APIs that it offers…

• Firebase ML Kit 101 : Text Recognition
• Firebase ML Kit 101 : Face Detection
• Firebase ML Kit 101 : Barcode Scanning
• Firebase ML Kit 101 : Image Labeling
• Firebase ML Kit 101 : Landmark Recognition
• Firebase ML Kit 101 : Language Identification
• Firebase ML Kit 101 : Smart Reply

The full source code can be found here!

hitanshu-dhawan/FirebaseMLKit

Thanks for reading! Share this article if you found it useful.
Please do Clap 👏 to show some love :)

Let’s become friends on LinkedIn, GitHub, Facebook, Twitter.