As the global tech market rises, the impact of machine learning is set to take a huge leap of 43.8% in the span of the next 5 years.
The concept is not really new but is still in its budding years where not many people/companies can take advantage of it due to the extravagant prices.
As the trend continues, ML has made its way from desktops and laptops to mobile phones and applications that are contributing to making daily tasks easier for the masses.
Flutter being a phenomenally famous cross-platform app due to its extensive features has also become a preferred platform for intelligent app production with ML integration by Flutter App Development Companies.
So here we are to help you roll with the development of Flutter apps using ML in the most efficient and effective way.
Let’s begin!
Integrating Machine Learning Kit in Flutter:
The start can be a little fussy if you are a complete fresher to it. For those who are familiar with the concepts of Firebase’s ML kit will get their way around easier.
Don’t worry, we shall cover the Firebase part in the sections later for all those who are new and wanna get a better hold of ML integration.
Moving on, here is everything you need for ML integration in Flutter App Development Services:
- Install Plugins
Install Flutter’s ML vision plugin from their official web address. It has been built by the Flutter app development team themselves.
You can choose from a variety of plugins based on your requirements. For example, if you need image identification features then you should use the image picker plugin that can help you choose either from the camera or the gallery.
- Create a Firebase Project
Set up a Firebase project by using Firebase’s Real-Time Database and Cloud Firestore.
You can use various APIs available with Firebase ML Kit and easily get your work done.
Implementation:
Implementing is easy and a three-step process:
- SDK Integration
Integrate all the SDKs using Gradle.
- Prepare your Input Data
If you are using a vision feature, you can capture and generate the necessary data with it.
- Apply the ML Model
Using the ML model with your data, you can generate a great variety of insights such as automatic metadata generation, photo embedding, etc.
Firebase & ML – For Learners
Keeping our promise, here we are to discuss the integration of ML with Firebase. Going through this section can help learners and beginners understand the basics of ML integration and easily apply it with Flutter app development as well.
Firebase is a mobile and web app development platform by Google. It provides a number of services and tools to work with and develop high-quality applications in return.
Developers quite love to create a database of their apps with Firebase as it is faster and requires less manipulation, especially when it comes to managing the infrastructure.
The best-known fact is that you can create or implement new functionality with just a few lines of code and that too without any deep knowledge of the model optimization or neural networks.
ML kit comes in handy for experienced developers as well as it comes with a set of convenient APIs for TensorFlow Lite models and mobile apps.
Firebase ML Kit Highlights:
Custom Model
In case you have your own ML model on TensorFlow, Firebase makes it easier for you to import your TensorFlow Lite models while also taking care of the hosting and serving as well. In short, Firebase acts as the API layer for your custom model.
Plethora of Libraries
Firebase ML comes with a huge set of ready to use APIs such as face detection, text recognition, bar code scanning, language identification, etc.
All you have to do is use the libraries as per your requirement.
Cloud Services
Firebase ML kit can work smoothly for on-device or cloud-based services. This means that no matter what the device or what the place is, you can work efficiently and securely anywhere, anytime.
Conclusion
The demand for cloud computing, Flutter app development services, and machine learning is expanding day by day. So much that you use it unconsciously on a daily basis in your life.
Some of the basic examples being:
- Google Lens – Yes, this is an application of machine learning as well. Launched in 2017, this app by Google lets you recognize images and give you appropriate information about them by simply hovering your camera lens over it. From flower types to dresses, animals, and more, need information, just hover!
2. Online Shopping Platforms – Every online shopping platform you use, from Amazon to Myntra, Snapdeal, or Flipkart, uses machine learning to give out appropriate product recommendations. How else did you think that whenever you are trying to buy a product online but somehow don’t, you end up seeing it everywhere? On Facebook, Instagram, or wherever you be scrolling at. All this nothing but Machine Learning at work.
3. Google Photos – We treat it just like a normal photo gallery, but it is so much more with the help of Machine Learning. From the face recognition feature that lets it recognize different people and categorize them accordingly to the place recognition feature, it’s all connected.
The list can be quite long, think of more apps that learn from the past inputs and improves their suggestions or functionality in time. All of them are bestowed with the magic of machine learning.
So don’t shy away from it, integrate machine learning in your apps now to enjoy a higher sense of adaptability and customization that users will love!
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