As a mobile developer, it is important for us to integrate some kind of intelligence into our apps for a better user experience. This blog is an overview of Machine Learning and Firebase ML Kit.

Machine Learning

Nowadays, machine learning has become an integral part of mobile development. Big organizations 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 better user experience

ML Kit for Firebase

ML Kit could be a mobile SDK that brings Google’s machine learning experience to robot and iOS apps during a powerful however easy-to-use package. whether or not you’re new or veteran in machine learning, you’ll implement the practicality you wish in exactly some lines of code. There’s no have to be compelled to have deep information on neural networks or model optimization to urge started.

At Google I/O 2018, Google announced Firebase ML Kit, a part of the Firebase suite that intends to give our apps the ability to support intelligent features with more ease. The SDK presently comes with a group of pre-defined capabilities that square measure normally needed in applications. Firebase ML Kit offers machine learning capabilities beneath a type of a wrapper, it conjointly offers their capabilities inside one SDK. ClickHere for domain page.

Key capabilities

• Production-ready for common use cases
• Deploy custom models

How does it work?

ML Kit makes it easy to apply ML techniques in your apps by bringing Google’s ML technologies, such as the Google Cloud Vision API, TensorFlow Lite, and the Android Neural Networks API together in a single SDK. Whether you need the power of cloud-based processing, the real-time capabilities of mobile-optimized on-device models, or the flexibility of custom TensorFlow Lite models, ML Kit makes it possible with just a few lines of code.

Currently ML Kit offers the ability to:

• Text recognition
• Face detection
• Barcode scanning
• Image labeling
• Object detection & tracking
• Landmark recognition
• Language identification
• Translation
• Smart Reply
• AutoML model inference
• Custom model inference

Implementation Path

• Integrate the SDK: Quickly include the SDK using Gradle
• Prepare input data: For example, if you’re using a vision feature, capture an image from the camera and generate the necessary metadata such as image rotation, or prompt the user to select a photo from their gallery.
• Apply the ML model to your data: Utilizing the ML model to your information, you can create a wide range of insights, for example, the enthusiastic condition of distinguished faces or the objects and ideas that were perceived in the image, contingent upon the component you utilized. At that point you can execute these insights to power includes in your app like photo frivolity, programmed metadata age.

In the end,
Play around with it and let us know what innovative ideas were you able to come up with using machine learning in your android applications.