In this fast-moving world, it is becoming more necessary to build applications that are intelligent to tackle the user based information. Machine learning works the way the data is provided to it therefore, it is quickly becoming an essential tool in App development.
Google at its I/O developer conference introduced ML Kit (Machine Learning Kit). With all the new package, it is a new software development kit (SDK) for app developers of iOS and Android.
Thus, it allows developers to integrate their apps with a number of pre-built Google-provided machine learning models. Talking about these models they support barcode scanning, text recognition, image labeling, face detection and landmark recognition.
Point to notice is that depending on network availability and the developer’s preference these models are available in both online and offline options. The real game-changers are the offline models that allows developers to integrate them into their apps and that they can use for free.
Reason Behind the Importance ML Kit
The reasons for the importance of the ML Kit are as follow. From the client point of view, the knowledge of mobile development will exceed knowledge of machine learning. That means it is the advantage for every mobile developer that has played around with Firebase.
Another one is that Google is making it easy to generate value with machine learning when it comes prebuilt models for common tasks. Due to these reasons, there will much more machine learning development in the enterprise in upcoming years.
Apart from that Developers will be less dependent upon the third party frameworks needed to complete basic tasks like barcode reading etc.
ML Kit SDK is a brand new software development kit which makes it quite easy to integrate ML models into mobile apps.
And no matter, whether you are a newbie or an experienced programmer you should know of basic features that include five ready-made APIs of ML Kit.
APIs of ML Kit provide applications with an ability to:
Ø Recognize text
Ø Scan barcode
Ø Recognize landmarks
Ø Detect faces
Ø Label images.
Let’s discover more about ML Kit.
With new ML Kit SDK, this process is dramatically simplified as compared to the earlier process. The thing is that you should know how to pass data to the API and wait till SDK will send a response.
As stated by Google’s team the implementing of their APIs don’t require deep knowledge of neural networks. The developers just need to add a few lines of code and enjoy new features in your app.
2. Custom models
This option of custom models is useful for experienced developers. If in case ‘base’ ML Kit APIs don’t fulfill developer’s needs, then they can introduce their own ML model.
Thus, the new ML Kit SDK works with Tensor Flow, iOS, and Android machine learning library as well. Therefore, it offers mobile developers with the possibility to download their own model to Firebase console.
3. Cloud and on-device APIs
Developers have a choice between cloud-based and on-device APIs. Therefore, it’s important to take into consideration some differences between these two options.
Talking about Cloud APIs, it recognizes objects more accurately as it processes data on the Google Cloud Platform. But, on the other hand, cloud models are larger in comparison to on-device ones.
Moreover, Offline models can work offline as they need less free space. That’s why they process data faster, but their accuracy is lower.
Google in their conference stated that their new SDK is cross-platform. That means developers can add APIs to both iOS and Android applications.
As a result, a robust competitor of Apple’s Core ML has arrived. But it can be experienced that CoreML still has advantages over the ML Kit.
The best part of ML Kit is that apps can run even in the old versions (Ice Cream Sandwich) of Android. Google offers better performance for devices with Android 8.1 Oreo and others.
Though getting started with machine learning can be difficult. But It is easy to imagine scenarios where machine learning on mobile App development makes sense.
With need development of technology, the world becomes an input device for the user. Things like Barcodes and text recognition can replace long, error-prone, and annoying text entry forms at many retail and business outlets.
Machine Learning is the foundation behind augmented reality. Thus, both Apple and Google will continue to provide the better solutions for the most common use cases related to user experience.