Leveraging The Applications Of Ai/ml In Developing Intuitive Mobile Apps

By Author: Josh Digi
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The applications of Artificial Intelligence and Machine Learning have been proven in virtually every industry, making this technology the most impactful innovation of the last decade. The AI/ML industry is predicted to reach a valuation of $190 billion by the year 2025.

Crunching huge swathes of data generated by modern digital devices, AI/ML have completely transformed consumers’ expectations from digital devices. In a time when consumers pretty much assume that whatever digital device, they use would provide them with a personalized service and make their life easier with intuitive functions, it is no longer a question of whether or not you should integrate AI/ML in mobile app development, but rather why you’re still mulling it over!

There’s already a slew of blogs on the myriad benefits and applications of artificial intelligence and machine learning, and a lot of literature on how this technology works. So, let’s focus on the mechanics of integrating AI/ML in your mobile apps.

Selecting the Right Tools
The tech stack for your ML powered app development must be based on various factors ...
... like your power and flexibility requirements and which of the applications of artificial intelligence and machine learning you are targeting.

Here are some great ML tools that range from offering ready-made solutions to allowing you to create your own models.

Core ML:
Apple uses its own ML framework Core ML for all its products. Integrated and compatible with the Vision, GameplayKit and NLP frameworks, Core ML enables real-time image and facial recognition, speaker identification and text prediction. Using a pre-trained ML model from the cloud, CoreML integrates it directly into any Xcode project by translating it into the CoreML format. Low latency, offline availability and excellent privacy are other benefits that set Core ML apart.

TensorFlow:
Google’s open-source ML framework Tensorflow is a bundle of multiple machine learning and neural networking models with a common metaphor. This enables it to have a wide array of possible applications like image recognition, natural language processing, word embeddings, hand-written digit classification and partial differential equation-based simulations, among others. Using a Python-based front-end API to develop apps, TensorFlow uses C++ to execute those apps. The updated version of TensorFlow now uses Keras API to train models.

Amazon Web Services Suite:
AWS offers a broad range of ML services to build, train and deploy ML models, along with the cloud infrastructure to support them. The AWS Rekognition provides image processing capabilities including text, object and facial recognition. For translations, there is AWS Translate; AWS Transcribe for speech to text, AWS Polly for text to speech and AWS Lex for chatbot solutions.

Cloud Vision/Firebase ML Kit:
Google’s Cloud Vision API is targeted towards image analysis, offering pre-trained ML models using REST and RPC APIs. For mobile app development, Google offers the Firebase ML Kit as part of the Cloud Vision API. Its capabilities include text, face, object and landmark detection, and also OCR scanning.

CNTK:
Formerly known as CNTK, the Microsoft Cognitive Toolkit is yet another open-source targeted towards distribute deep learning. With CNTK you can create and combine various types of models like convolutional neural networks, recurrent neural networks, and feed-forward DNNs. You can use the CNTK library in your programs based on C#, C++ or Python. The CNTK toolkit also allows you to choose your algorithms, metrics, networks, making it highly customizable.

PyTorch:
Facebook’s PyTorch is a Python and Torch based deep learning library for processing tensors (fundamental units of data) on GPUs. Highly preferred by Deep Learning researchers, this open-source library helps solve complex mathematical problems and is great for applications like NLP.

OpenCV:
This open-source library meant for image processing can analyse images and videos for facial, object and handwriting recognition and many more use-cases. Supporting both iOS and Android, OpenCV features C, C++, Java and Python interfaces.

Crucial Aspects of AI/ML Integration in Mobile App Development

Decide your area of focus
Before you get busy with the ML integration part, you must first freeze your area of focus—be it Personalized recommendation solutions, Customer support, Retail, Supply chain, Security or any other domain. However, whatever domain you go with, chatbots are an absolute must. If you don’t wish to create your own, you can get help from any of the leading artificial intelligence companies.

Ensure your data is proper
For AI/ML app development, it is critical to have a complete understanding of your incoming data. False data or duplicate data collection devices could mess up your model. Refine and organize the data collection processes and the data itself to ensure the best possible processing.

Gauge your AI’s performance with metrics
Whatever domain you plan to deploy your AI in, you must be absolutely clear on the specific end objective for it, with a metric set up to measure it. While you train your AI model on the procured data sets, as per the set metrics, you must also make sure to benchmark all of your data and take steps so that visual cues are not misinterpreted.

Never be overly reliant of APIs
Although APIs can be great as temporary solutions or for testing purposes, you should not think of them as a full-fledged solution. If you’re looking for a long-term AI/ML solution, it would do you a world of good to invest the resources to create your own and train it yourself.

Closing Thoughts
AI/ML integration should now be an indispensable facet of mobile app development, and the technology will only continue to get increasingly advanced. In light of the tremendous business opportunities presented by AI and ML for business leaders, its adoption in your mobile solutions is an absolute no-brainer.

Blending the highly perplexing world of AI/ML technology with that of mobile apps requires expertise held by an artificial intelligence development company, along with experience in app development. With both of these big guns in its arsenal, Josh Software would be the perfect partner for engineering your AI/ML app development project.

Add excellent value to your business with next-gen AI/ML powered apps by Josh Software.

Reach out to know more.
https://joshsoftware.digital/