How We Deliver AI App Development: End-to-end Process

1. Define the Business Problem First

Before developing an AI-based software solution, you need to define the core problem your business faces, such as whether you want to:

• automate repetitive workflows,
• increase personalization, or
• enable real-time analytics.

This involves spending serious time outlining the scope, potential impact, and desired outcomes of building an AI application.

Think about how this AI solution can add value to your users’ lives or benefit your organization internally.

Skipping this problem definition phase means you’re going to compound the risks later in the software development lifecycle.

No problem = No product

For example, a logistics company may want to reduce route optimization time, while a financial institution may want to predict loan default rates based on customer data. You need clarity on problems like where the most time is spent on manual tasks and that’s where AI can shine.

2. Data Collection

The next step in AI application development is not just about generating chunks of output;it’s about asking:

• what datasets the AI model is trained on,
• which data the AI uses to generate output, and
• whether you’re using publicly available data.

If you train AI on the wrong data, even a sophisticated model will produce inaccurate outputs. That’s why the data you use to train your AI model should be clean, relevant, and well-structured. 

It can be done by removing duplicates, labeling datasets correctly, and handling missing values.

For natural language processing tasks, Common Crawl is commonly used; it was even used for OpenAI’s GPT-3 model. 

Another way to access high-quality datasets is through open dataset platforms such as Kaggle and AWS Data Exchange.  

This eliminates the need for engineering teams to collect data from scratch, saving significant time and effort.

3. Choosing the Right Tech Stack

Here comes the part of choosing the right technology tools for training your AI models that align with your business vision and can be integrated well with existing infrastructure.

For example, Python is the go-to programming language for AI development because of the rich ecosystem and strong community support it provides.

Here are some popular frameworks and tech stacks that can be used for building AI and Gen AI applications:

4. Designing, Training, and Fine-Tuning the AI Model

Training is just a part of the product development process. While monitoring the performance of an AI model is another part of the story.

Here’s how you can proceed:

(a) Choose the right training technique

(b) Select the model architecture

• Convolutional neural networks for image and video analysis
• Recurrent neural networks for speech recognition and translation tasks
• Generative Adversarial Networks for performing specialized tasks such as image generation and video enhancement

(c) Prepare the data for training

Split data into training datasets (such as 70/15/15 datasets)

(d) Training and optimizing the model

• Feeding data: Train the model through multiple iterations
• Hyperparameter tuning: Adjust the model parameters based on improvements in the model’s performance
• Cross-validation: Split the data multiple times, training and testing the model on different parts each time. This gives a true picture of how the model will perform on unseen data.

5. Integration of Model into the App

Once the model is trained and validated, the next step is integrating models into your app ecosystem.

Here, you need to decide whether to integrate the AI model into the frontend or backend part of the application.

Frontend integration is required for enhancing user experience, while backend integration is well-suited for performing complex tasks such as speech recognition.

For handling backend requests, this involves setting up the model and containerizing it using Docker or TensorFlow Serving.

Note: For building AI-powered mobile apps, optimizing model performance is crucial for low latency. 

If the app takes too long to provide recommendations, users are likely to abandon it, especially when running the model on the cloud or on-device.

6. Testing the AI Model

Training models should not be treated as a one-and-done task. 

Every time the data changes even slightly, you need to revalidate and test the model so that it’s relevant to the current scenario. 

These models are imperfect when starting out, but to keep them up-to-date, they need regular retraining to avoid model drift as performance declines when data patterns change.

Here are three methods you can use to test the AI app:

7. Deploy the Model

Post-launch, you need to refine the model performance and retrain it so that it can perform well in the current environment. 

In this stage, user feedback will be collected to see whether the model meets user preferences and solves their pain points.

If not, an iterative development approach will be used to improve its performance based on user feedback and engagement metrics.

8. Improve the Model Performance Based on User Feedback

After testing is conducted, you need to choose the platform where you will deploy it such as Android/iOS or Google Cloud Platform.

The deployed applications should be continuously monitored to optimize app performance and updated, as updates roll out to minimize downtime and enhance user experience.