AI Project Planning and Real-World Applications (Lecture 20)

This is the final lecture of our 20-part series. We’ll conclude by discussing how to plan, design, and execute AI projects in real-world scenarios.
You’ll learn about the AI project lifecycle, practical applications in various industries, and how to deploy models into production.

Table of Contents

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1) AI Project Lifecycle

AI projects go beyond just training a model. They require a complete end-to-end strategy:

1. Problem Definition (Business Understanding)

   • What problem are we solving?
   • Example: Automating customer service, demand forecasting, or translation.

2. Data Collection & Cleaning

   • High-quality data is critical for model performance.
   • Includes text, images, logs, and multimodal sources.

3. Model Selection & Training

   • Choose between supervised, unsupervised, or reinforcement learning.
   • Fine-tune pretrained models (e.g., BERT, GPT, CLIP).

4. Deployment & MLOps

   • Expose models via APIs, web apps, or mobile apps.
   • Monitor performance and update models regularly.

2) Real-World Applications

NLP (Natural Language Processing)

• Chatbots for customer support (GPT-based assistants)
• Document Summarization for news, reports, or research

Computer Vision

• Quality Inspection in manufacturing
• Medical Imaging for diagnosis support

Multimodal AI

• Image Retrieval (search images with text queries)
• Autonomous Driving (combining vision + sensor data)

3) Hands-On Example: Designing a News Summarization Service

Imagine building an AI project for automatic news summarization:

1. Define the Problem → Provide concise summaries to readers
2. Collect Data → Crawl news articles and preprocess text
3. Model Training → Fine-tune BART or T5 for summarization
4. Deploy → Expose via Flask/Django API and integrate with UI

Example Flask API:

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from transformers import pipeline
from flask import Flask, request, jsonify

app = Flask(__name__)
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

@app.route("/summarize", methods=["POST"])
def summarize():
    text = request.json.get("text")
    summary = summarizer(text, max_length=50, min_length=10, do_sample=False)
    return jsonify({"summary": summary[0]['summary_text']})

if __name__ == "__main__":
    app.run(debug=True)

4) Key Takeaways

• AI projects must align with business problems, not just technology
• Data quality is as important as algorithms
• MLOps ensures stability, monitoring, and scalability in production
• Start small, then expand applications as confidence and results grow

5) Series Recap

Across 20 lectures, we’ve covered:

• AI and ML fundamentals
• Neural Networks, CNNs, RNNs, LSTMs, GRUs
• Attention, Transformers, BERT, GPT
• Multimodal AI (Text + Image)
• Project planning and applications

With this foundation, you are equipped to plan and execute AI projects in real-world settings.