Deploying Machine Learning for Android Applications Development

Sanam Malhotra | 7th January 2020

Mobile application projects demand intense research and development efforts. From ideation and planning to design and development, mobile developers often struggle with proper evaluation of project requirements and value generation. Artificial intelligence (AI) is opening new development opportunities with dynamic machine learning capabilities such as predictive analytics, facial detection, and more. In this blog post, we are exploring the implementation of machine learning for android app development using several AI and Machine learning development services.


Machine Learning  + Traditional Programming = Smart App Development

Programming has fueled some of the world’s most innovative software developments for almost a century. From the Short Code to Python to cloud computing, programmers and software developers have propelled the global digital transformation. The advent of AI development services has opened new opportunities for developers using machine learning techniques for more efficient, automated, and intelligent application development.

machine learning vs programmingAlthough the model training approach under machine learning and programming is quite different, together these methodologies boost model development significantly.

While in its embryonic stage, machine learning is making diligent efforts to overcome the limitations of traditional programming practices. Machine learning algorithms work as a supplement for programmers to accelerate application development along with the following benefits-

a) Lower Latency

b) Improved Security and Privacy

c) Reduced Operational Costs

d) Increased interoperability and scalability

Also read- Gearing up for Machine Learning Trends in 2020


Embedding Machine Learning Features into Android App Development

1) Predictive Analytics with Flask

AI’s data processing abilities have enabled businesses to predict future events with accuracy. Predictive analytics is an ML-based technique that strengthens the decision-making capabilities of software development businesses with-

a) Precise estimation of project resource requirements

b) Evaluation of function points, time consumption, and code language source.

c) Assessing the complexities of a project and its story points.

Flask is a Python-based web framework that supports the development of websites and applications. Flask is an easy and convenient platform to develop Restful APIs in python for integrating predictive analytics in web applications. JSON files can be used to synthesize data and train the model for-

a) Real-time predictions to plan android app development strategically

b) Dedicated predictive analytics for function-specific android apps such as accounting, healthcare, and marketing purposes.

c) In-depth data analysis using Android Pie’s Neural Network APIs to augment on-device machine learning efforts.

d) Improved mapping and location services by integrating comprehensive REST APIs.


How does Oodles AI deploy machine learning for predictive analytics applications?

At Oodles, we build business-oriented AI-powered applications to automate critical operations and processes. Our AI team is currently working on a machine learning model that is supported by Amazon’s Sagemaker (a cloud service). We have trained the model with raw CSV data that is labeled using function-specific AI algorithms. Its primary objective is to extract actionable insights and predictions about potential resources under the surface of the earth. To achieve this, we have used date from a sensor-based drilling machine to train the model and Flask to integrate the same in web applications.

Here’s a blueprint of the model-

 predictive analytics ai android app

Flask enables efficient implementation of this machine learning model across channels.


2) Face and Object Detection with Google Firebase

What started as a social media fad for SnapChat Filters turned into an essential security architecture for business infrastructures and digital devices. Facial recognition and object detection applications powered by artificial intelligence have significantly automated and improved authentication facilities for-

a) Real-time video analytics for surveillance

b) Automated quality inspection

c) Fraud detection

d) Real-time customer engagement and brand awareness

Google Firebase is an ML-based mobile SDK (Software Development Kit) that enables mobile developers to integrate machine learning capabilities into applications. Firebase is packed with several pre-built APIs for text recognition, facial and object detection, image labeling, smart responses, and more. Firebase’s Android Studio instantly connects an android application with ML Kits followed by an easy configuration of AndroidManifest.xml files, camera, and images.

machine learning firebase android app

As apparent, Firebase provides a step-by-step integration of various ML Kits for-

a) Recognizing and labeling images

b) Recognizing text in images

c) Detecting faces in images and videos

d) Identifying barcodes

Also read- Why Scikit-learn is Optimum for Python-based Machine Learning


Implement Machine Learning for Android App Development with Oodles AI

We, at Oodles AI, have experiential knowledge in deploying machine learning algorithms and frameworks for dynamic app development. Our machine learning development capabilities extend to chatbot development, computer vision, in-depth analytics, natural language processing, and RPA services. Our machine learning development services are available for both on-premise and cloud-based business infrastructures including AWS, Google, IBM Watson, and Microsoft Azure consulting services.

Talk to our AI development team to know more about our artificial intelligence services. 

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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