How to Create an AI Application: Steps, Challenges and Solutions

Have you ever questioned how self-driving cars navigate without human input, how chatbots can carry on conversations, or how Netflix seems to know precisely what you want to watch? Artificial Intelligence (AI) holds the solution. Businesses of all sizes are eager to leverage the potential of AI-powered applications as they become increasingly accessible and affordable. Many are exploring how to create an AI application to automate tasks, make smarter decisions, and stay ahead in a competitive market.

If you’re looking for a clear, step-by-step guide on how to create an AI application, you’re in the right place. In this blog, I will outline steps such as data handling, model development, and deployment, using an image caption generator as a practical example, and leverage AWS services.

Also addresses common challenges such as data privacy, costs, integration, and lack of expertise, offering actionable solutions. Ultimately, continuous monitoring and improvement are crucial to the successful implementation of AI across various sectors.

• How to Create an AI Application?
• Example: Building an AI-Powered Image Caption Generator Using AWS
• Summary of the AI App
• Common Challenges in Creating an AI App and How to Overcome Them
• FAQs

How to Create an AI Application?

It takes more than just coding to create an AI application; you also need to choose the appropriate AI tools, solve a real-world problem, and integrate them into a working system. Starting with a specific goal is crucial, regardless of whether you’re working with computer vision, natural language processing, or machine learning.

Consider, for instance, the concept of automating the creation of LinkedIn captions from photos. Here’s how it works: a user uploads a picture, AWS Rekognition steps in to pull out relevant labels, and then a Bedrock model takes these labels to craft a LinkedIn caption. This app is a great illustration of how AI can make content creation easier, enhancing the way we manage social media.

Now, let’s walk through the process using this example as our guide and understand how to create an AI Application.

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Identifying Business Problems that AI can Solve.

The first step in building any AI app is to define the problem you’re trying to solve clearly. AI excels at solving specific problems, such as managing tedious and repetitive tasks or assisting in decision-making. Ask yourself these questions before you start coding:

• What exactly am I trying to solve here?
• Does AI really solve this problem better than traditional methods?
• How will users or businesses in the real world benefit from this AI thing?

Example: People on social media often struggle to come up with interesting captions for their posts. It’s time-consuming and sometimes frustrating. They can save valuable time by using AI to automate this process while still receiving accurate, pertinent captions that accurately reflect the content of their photos. 

Choosing the right AI Platform

Choosing the right AI platform and AI tools follows after the identification of business problems. In terms of AWS Cloud, it provides AWS SageMaker for the training of custom models, Amazon Bedrock for generative AI, and Amazon Rekognition for visual analysis. Additionally, the platform and tools you choose will depend on the kind of problem you’re trying to solve.

Before choosing a platform, consider these questions:

• Should computer vision be used to process images, natural language processing (NLP) to understand text, or machine learning (ML) to analyze data patterns?
• Do I need to train a custom model, or can I just use an AI service that has already been trained?
• Does the service fit my requirements in terms of cost and scalability?

Example:  Amazon Rekognition helps extract meaningful labels from an image, and Amazon Bedrock generates a LinkedIn caption using AI-powered text generation models.

Data Collection and Preprocessing.

The AI ​​applications rely on high-quality data. Before training a model, you need to clean the relevant data and ensure accuracy. The process includes:

• Data Collection: Collecting relevant images, text, or other input data.
• Data cleaning: Removing errors, duplicates, or irrelevant data.
• Data labeling: Assigning meaningful tags to improve model learning (this is required for custom models).

If you’re training a model from scratch, you will need a dataset to teach it how to identify the pattern. However, if you are using pre-trained AI models, this step is often simple.

Example: Since we are using Amazon Recognition (a pre-trained AI model), we do not need to collect or label data manually. Instead, Rekognition automatically analyzes the uploaded images and extracts a meaningful label, which is later passed to Amazon Bedrock for caption generation.

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Model Training and Development.

Alright, so you’ve got your data all cleaned and ready to go. Now the exciting part: training your AI model. Here’s what you do:

• Selecting the proper model: To begin with, you have to choose the appropriate model. You may use a pre-trained model or design your own custom model. It is like selecting between buying a car from a dealership or creating one yourself; both have their merits.
• Training the model: Next, you’ll need to train your model. That’s a question of exposing it to labeled data and fine-tuning its parameters to get the maximum accuracy. It’s similar to training a puppy: you command it to do this, you reward it when it does it correctly, and you punish it when it gets it wrong.
• Measuring performance: Now it’s time to try out how well your model performs. You’ll run it on new data to see if it’s producing any errors or biases. It’s like testing your puppy to see if it remembers its training.

Now, if you’re on AWS, you have some AI Services that you can use. You have, for instance, Amazon SageMaker, where you can train your own custom AI and machine learning models. And then you have Amazon Bedrock, where you can use pre-trained generative AI models.

Example: Rather than training a model from the ground up, I utilize Amazon Bedrock, which already knows how to generate text. It takes the extracted image labels from Amazon Rekognition and creates captions without requiring any training.

Deployment and Integration into Existing Systems.

Okay, so your AI model is all trained and ready to rock. Now it’s time to put it into action in the real world. Here’s what you need to consider:

First, things first, you need to determine where you’re going to host your AI model. You have a few places you can go here, such as 

• Hosting the AI model: Deploying on cloud services like AWS Lambda, ECS, or SageMaker.
• Building APIs: Then, you’ll have to create APIs. These are the bridges that enable other apps to communicate with your AI model.
• Integrating with existing systems: And then, of course, there’s the matter of integrating your AI model into your current systems. You want to ensure it can communicate smoothly with your databases, applications, or websites.

So, in summary, rolling out your AI model is really all about finding it the perfect home, constructing communication bridges, and ensuring that it gets along with the rest of your tech relatives.

Example: In my  case, the backend is developed with AWS Lambda, which serves as the intermediary between the frontend and AI APIs. When the user uploads a photo, Lambda invokes Amazon Rekognition to scan the image and retrieve corresponding labels.

These labels are then passed on to Amazon Bedrock, which utilizes AI-based text generation to develop an appropriate LinkedIn caption. Ultimately, the generated caption is passed back to the user.

Monitoring and Continuous Improvement.

Yes, after you get your AI Application going, the work isn’t over yet. You need to monitor it to ensure it’s working effectively and keeping itself accurate. This is what you need to do:

• Tracking performance metrics: First, you’ll need to monitor performance metrics. That is, you’ll need to monitor response times, accuracy, and user feedback.
• Updating the model: Second, you may need to refresh the model. This might involve retraining it with new data to enhance its outcomes.
• Error handling and debugging: And then there’s debugging and error handling. If it breaks, you’ll have to repair it.

AWS has some useful tools to assist with this. For instance, 

• Amazon CloudWatch, which enables you to monitor the performance of your model.
• AWS Lambda & API Gateway logs, which you can use to debug and monitor API requests.

Example: If our users find our AI-generated captions inaccurate or off-topic, we can gather their feedback and adjust our strategy accordingly. This could mean enhancing how we pull labels, adjusting how we engineer prompts, or even experimenting with a different Amazon Bedrock model for text generation.

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Example: Building an AI-Powered Image Caption Generator Using AWS And Streamlit

This project demonstrates how to utilize AWS services such as Amazon S3, Amazon DynamoDB, AWS Lambda, Amazon Rekognition, and Amazon Bedrock together with Streamlit for a simple web interface to build an AI-driven Image Caption Generator. Users can upload images to the application, let AI generate captions for them, and then fetch the captions later.

Setting Up S3 Bucket and Event Trigger for Lambda

To begin, you need to create an Amazon S3 bucket where users can upload images. This bucket will store the images and trigger a Lambda function whenever a new file is uploaded. After creating the bucket, configure an event notification that listens for new object uploads and invokes the Lambda function. This ensures that each time an image is added, it automatically triggers the AI pipeline for processing.

Setting up the Environment

Installing dependencies and setting up a Python virtual environment are requirements for running the application. The following commands will help you prepare your system.

These commands:

• Update your system packages.
• Install pip and Python 3.
• Build a virtual environment
• Switch it on.
• Install Boto3 (for AWS SDK) and Streamlit (for UI).

Note: The following commands have been tested on an AWS EC2 instance running Ubuntu 24.04.

sudo apt update
sudo apt install python3 python3-pip -y
sudo apt install python3.12-venv -y
python3 -m venv myenv
source myenv/bin/activate
pip install streamlit boto3

Building the Streamlit UI

The Streamlit UI allows users to upload images, store them in AWS S3, and fetch AI-generated captions from DynamoDB. The code snippet below builds the frontend and handles image uploads. After uploading an image, users can retrieve AI-generated captions from DynamoDB by entering the image filename.

Note: You may have to change the Bucket name in the following code.

Create a file named ‘image-caption-generator.py’ and save the following code in it.

AWS Lambda Function for Caption Generation

When an image is uploaded to S3, an AWS Lambda function is triggered. This function uses AWS Rekognition to detect labels and AWS Bedrock (Claude AI) to generate captions. When an image is uploaded, Lambda extracts labels, generates captions, and stores them in DynamoDB.

Create a Lambda function, save the following code, and deploy it. Ensure that the IAM role assigned to this Lambda has the necessary permissions to interact with Amazon Rekognition and Amazon Bedrock.

import json
import boto3
import os
from urllib.parse import unquote_plus

rekognition_client = boto3.client('rekognition')
dynamodb_client = boto3.resource('dynamodb')
table_name = "ImageCaptions"
table = dynamodb_client.Table(table_name)
bedrock_client = boto3.client('bedrock-runtime')

def generate_caption(prompt_text, temperature):
   response = bedrock_client.invoke_model(
       modelId="anthropic.claude-v2",
       body=json.dumps({
           "prompt": f"\n\nHuman: {prompt_text}\n\nAssistant:",
           "max_tokens_to_sample": 500,
           "temperature": temperature,
           "top_p": 0.9
       }),
       contentType="application/json",
       accept="application/json"
   )
   body = json.loads(response['body'].read())
   return body.get('completion', '').strip()

def lambda_handler(event, context):
   print("Event received:", json.dumps(event))
  
   bucket_name = event['Records'][0]['s3']['bucket']['name']
   image_key = unquote_plus(event['Records'][0]['s3']['object']['key'])
  
   try:
       # Step 1: Detect labels
       response = rekognition_client.detect_labels(
           Image={'S3Object': {'Bucket': bucket_name, 'Name': image_key}},
           MaxLabels=5,
           MinConfidence=80
       )
       labels = [label['Name'] for label in response['Labels']]
       print("Labels detected:", labels)

       prompt_text = f"""
       Generate exactly 3 professional, concise, and engaging captions suitable for a LinkedIn post. 
       Base these captions on the following image labels: {', '.join(labels)}. 

       Strict instructions: 
       - Sound polished and professional 
       - Reflect a positive, inspiring tone 
       - Be relevant to career growth, achievements, teamwork, or leadership 
       - Keep each caption short and impactful (1-2 lines) 
       - Do not use hashtags, emojis, or repetition 
       - Do not mention that these are captions or explain them in any way 

       Respond strictly with only the 3 captions in a numbered list. 
       Do not include any explanations, introductions, or additional text in your response.
       """

       # Step 2: Generate three different captions
       captions = []
       for temp in [1.0]:
           caption = generate_caption(prompt_text, temp)
           print(f"Caption (temp {temp}):", caption)
           captions.append(caption)
      
       # Step 3: Store in DynamoDB
       table.put_item(
           Item={
               'image_key': image_key,
               'captions': captions
           }
       )
      
       return {
           'statusCode': 200,
           'body': json.dumps({'captions': captions})
       }
  
   except Exception as e:
       print(e)
       return {
           'statusCode': 500,
           'body': json.dumps({'error': str(e)})
       }

Running the Streamlit Application

To start the Streamlit web application, use the following command:

streamlit run image-caption-generator.py

If you want to run it in the background, use:

nohup streamlit run image-caption-generator.py --server.port 8501 > streamlit.log 2>&1 &

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Summary of the AI App

This app is a simple yet efficient use case of an AI-based application that automatically creates professional captions for pictures using AWS services. It uses Amazon S3 to store images, Amazon Rekognition to detect labels, AWS Lambda for serverless computation, Amazon DynamoDB to save captions, and AWS Bedrock (Claude AI) to create captions.

The application follows these steps:

1. Upload an Image: Users upload an image through a Streamlit web interface, and it gets stored in an S3 bucket.
2. Trigger AWS Lambda: The upload event triggers a Lambda function to process the image.
3. Label Detection: Amazon Rekognition extracts primary labels from the image.
4. Caption Generation: The labels are passed as a prompt to Claude AI (AWS Bedrock) to create three professional captions.
5. Store Captions: The captions get stored in DynamoDB for retrieval.
6. Retrieve Captions: Users can input the image name and retrieve the generated captions from DynamoDB through the Streamlit interface.

This project is a simple AI application use case that illustrates how cloud-based AI services can be leveraged to develop a smooth, automated, and scalable solution for image processing and captioning.

Common Challenges in Creating an AI App and How to Overcome Them

I understand making an AI application is thrilling, but let’s be realistic here. Things never remain the same. I have encountered a series of challenges that many teams face when dealing with AI applications. However, it’s worth noting that with the right strategy, it’s possible to overcome these challenges. Let us look at the most prevalent difficulties in AI development and how you can overcome them.

Data privacy concerns and regulatory compliance.

Data privacy was the very first concern I encountered while working with AI. As you may know, AI models rely on vast amounts of data, whether it is user or business data. But good data also has great responsibility and must be handled cautiously in compliance with legislation such as the CCPA, GDPR, and HIPAA.

How to overcome

• Use Anonymization Methods:
  Before you load your data into AI models, you can use techniques like data masking, tokenization, and differential privacy to conceal sensitive data.
• Implement Role-Based Access Controls (RBAC):
  You can restrict access to data to certain members of a team so that it is not available to everyone.
• Stay Up-to-Date on Regulations:
  AI regulations evolve, so track compliance updates to avoid getting into trouble. I found it useful to consult legal experts or compliance teams when handling sensitive data.
• Apply Privacy-Preserving AI Techniques:
  Federated learning and homomorphic encryption can be employed to train models without publishing raw data.

High development costs and budget considerations.

Let’s face it, developing AI isn’t particularly inexpensive. The expenses quickly mount up, including storage, cloud computing, and hiring knowledgeable AI experts. Believe me, when I first started, I had no idea how much training large models would actually hinder me.

How to overcome

• Start with Open-Source Models:
  I started with pre-trained models from sites like Hugging Face, TensorFlow Hub, and OpenAI rather than creating everything from scratch, which is incredibly costly.
• Optimize Cloud Costs:
  Then I learnt more about cloud expenses. All of the main providers—AWS, Azure, and Google Cloud—offer free tiers or more affordable choices, such as spot instances, that can result in significant cost savings.
• Prototype Before Scaling:
  Before investing a lot of money in a large-scale deployment, test your idea with a very small proof-of-concept (PoC).
• Explore Grants and AI Funding:
  Some organizations offer funding for AI research and development, so you can explore such opportunities.

Integration with legacy systems.

Even though AI sounds futuristic, the majority of businesses still rely on legacy systems. Making AI compatible with Customer Relationship Management (CRM) software, Enterprise Resource Planning (ERP) systems, or legacy databases is a nightmare when legacy infrastructure is involved.

How to overcome

• Use APIs for Seamless Integration:
  Create APIs to incorporate AI models into current apps rather than rewriting traditional systems.
• Leverage Middleware Solutions:
  Tools like AWS Glue, MuleSoft, or Apache Kafka can serve as translators between your modern AI systems and those outdated databases. They handle the complicated data transformation and routing that makes integration possible.
• Incremental Adoption:
  Rather than attempting a complete system overhaul, introduce AI incrementally – department by department or process by process. This gives your team time to adapt while minimizing disruption to daily operations.

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Lack of AI expertise in-house and hiring solutions

Finding qualified AI talent is a real struggle these days. Not every company has its own AI department, and competing for top-notch AI engineers, data scientists, and MLOps experts can get both expensive and frustrating. I’ve seen firsthand how talent shortages can derail project timelines.

How to overcome

• Start by looking inside your organization:
  Your current employees could be your greatest assets – put money into upskilling them using resources such as Coursera, Udemy, or any other platform. Sometimes your best AI people are already employed on your books, just awaiting some chance to develop.
• Partner with Nearshore or Offshore AI Teams:
  Offshore teams from nearby countries can provide a sweet middle ground of affordability and ease – you have skilled talent without the wide time zone disparities that kill communication.
• Use No-Code or Low-Code AI Platforms:
  If you don’t have in-depth AI skills on staff, platforms like AWS SageMaker Canvas, Google AutoML, DataRobot, and Azure Cognitive Services enable you to create working AI models without a PhD in machine learning.
• Hire Freelancers or Consultants:
  For individual projects, freelancers and consultants can be a godsend. Hiring in specialists for a short-term project usually costs less money than bringing on full-time experts you may not require on an ongoing basis.

Conclusion

Based on my experience, creating an AI Application is not simply about training a model; it is something more. It involves recognizing the correct business problem, selecting the most suitable AI platform, gathering and preprocessing high-quality data, and ensuring a smooth deployment and integration. All these steps play an important role in building a solution that delivers real value.

However, issues such as data privacy, high development costs, and compatibility with legacy systems can pose significant impediments. With regulatory compliance ensured, cost-efficient AI solutions adopted, and no-code or low-code platforms, such as AWS SageMaker Canvas, utilized, organizations can simplify the process. Additionally, hiring nearshore AI expertise or upskilling existing teams can help address the knowledge gap.

Building AI solutions is an ongoing process. Once deployed, continuous monitoring and improvement are critical for long-term success. If you want to stay ahead of the competitive landscape, now is the perfect time to learn how to create an AI Application that drives innovation, performance, and tangible global impact.

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