Introducing Firebase MLKit
In the pantheon of software companies, Google clearly stands out, particularly because of its innovative products. Just like every year, programmers and coders across the globe attended the Google I/O conference in 2018. They were all anxiously waiting to see the next exciting innovation from Google. Finally, came the moment when Google unveiled the element of surprise. Google presented ML Kit for Firebase in its I/O conference in the year 2018.
Here, in this article, we will talk about machine learning solutions for developers such as ML Kit for Firebase. This Kit was already presented to the users at the Google I/O conference in 2018. The following year, it got some updates too.
With millions of apps available in the Google Play Store, Android App Development has become the backbone of businesses these days. Let’s read how can you use machine learning in your apps to solve real-world problems.
Here is how you can use Firebase ML Kit in Android. Let’s review this SDK to explore its advantages, features, and key capabilities. The blog speaks about the benefits of using Firebase ML Kit, its features, capabilities, and how it works.
What is Firebase ML Kit?
ML Kit is a mobile SDK that helps you use ML techniques in your Android and iOS applications. It is an easy-to-use yet very powerful package that brings Google's machine learning expertise to Android and iOS apps.
You don’t need to have prior knowledge of Machine Learning techniques and algorithms for using Firebase MLKit in Android and iOS apps. Therefore, no matter, whether you are new or experienced in Machine Learning, you can easily run the functionality that you need in a few lines of code.
Using Firebase MLKit in Android - Key Capabilities
So, what are the benefits of using Firebase ML Kit? There is no doubt that using a Firebase ML kit in Android can help solve many real-world problems. The Firebase ML kit uses Machine Learning techniques to find appropriate solutions for resolving the issues. Currently ML Kit has the ability to solve these issues.
You might be wondering as to how you can use Firebase ML Kit. In order to utilize these features of Firebase ML Kit, you are required to pass the selected data to SDK. In return, you will get back the data depending upon the part of ML Kit you desire to choose.
In simpler words, the data that you will receive back will be dependent on the machine learning capability being used. So, you just need to extract and then use the data from the response that is returned back to you.
Using Firebase MLKit in Android - Key Benefits
This was all about how you can use Firebase ML Kit. It is used to recognize text, scan barcodes, label images, recognize text, and face recognition. Besides these, you can use the Firebase ML Kit for other requirements too. Here are some alluring benefits of using Firebase ML Kit. These include,
-Using Firebase MLKit in Android allows you to upload your own custom TensorFlow lite models. So, you need not worry about the hosting of these models to your user’s devices.
-Moreover, Firebase ML offers it’s machine learning abilities both on the device and on the cloud. This enables you to be more thoughtful and innovative while using the advanced techniques of machine learning.
Using Firebase MLKit in Android - Services
A. On-Device and Cloud-Based Services!
While using Firebase ML Kit, you can have two choices. You can either use on-device services or you can choose Cloud-based services for leveraging ML capabilities.
On-Device: The advantage of using on-device services of Firebase MLKit is that it will work fast and will be able to give results even if there is no network connection present.
Cloud-Based: You can achieve greater accuracy by using cloud-based APIs, as it utilizes the Google Cloud platforms and machine learning technology for an increased level of accuracy.
B. When to Use On-Device and Cloud-Based Learning?
How to decide which service to choose? You can easily choose among the two, on-device, or cloud-based depending on some factors. The decision to use which service for implementing the features include,
-Method of learning being used
-Connectivity of the device
-The quality of the connection
If you decide which service to choose based on these factors, then Firebase ML Kit can help you in implementing the features. Below is a list that shows which capabilities are available on-device and on the cloud.
Using ML Firebase in Android - How does it Work?
So, here is how you can use Firebase ML Kit. After adding the Firebase to your project, you need to add the base vision dependency to your project in order to start using the ML Kit.
This dependency is required by each of the ML capabilities. Here, at this point in time, you will be having the visual tools that are present for your app. Using these tools, you will be able to provide the input data to the model from your application.
This can either be the data for barcode scanning, text recognition, face recognition, etc. Based on this data, the ML Kit will provide back some result value that can be applied to your application. Now, you need to input the data to the model from your application. ML Kit then provides back the result that you can apply to your application.
The ML model will be downloaded by default for on-device when it’s used by your application for the first time. In case, you wish to download the required models at install time, you can do that by adding the following metadata to your manifest:
android:value="barcode, face, other_model_names..." />
Now, adding the metadata completely depends on your application. If the ML model is the main part of your application, then the models should be downloaded as they are.
Using ML Firebase in Android- Key Challenges!
While using Firebase MLKit in Android, you may face certain issues. Some of the reasons because of which, you may face certain difficulties while doing tasks using Firebase ML are,
-Lack of complete knowledge about Machine Learning may not allow you to implement the required features using Firebase ML Kit.
-It’s difficult to find accurate and well-trained Machine Learning models. The process becomes more difficult if you have to optimize these models for your platform.
-Hosting the ML model for cloud access may sometimes become difficult for ML implementation of the different features.
These are some of the benefits of using Firebase ML Kit. Hope, after reading this you have now got a clear idea of the key features, capabilities, benefits, and challenges being faced while using Firebase ML Kit in Android.