README File Entry 📝

• Problem Statement: Marketing departments struggle with extracting actionable insights, targeting audiences, and identifying trends.
• Solution: Fine-tuned a Tiny T5 model (SLM) for marketing insights generation.
• Why SLM?:
  • Reduced cost compared to LLMs
  • Can run on CPU, no GPU required
  • Requires less data for fine-tuning
  • Faster inference and deployment
• Model Details:
  • Sequence-to-Sequence (Encoder-Decoder) architecture
  • Fine-tuned on instructional dataset for marketing domain
• Key Learnings:
  • Transformer architecture and attention mechanisms
  • Casual vs Sequence-to-Sequence models
  • Encoder-Decoder architecture and applications
  • Tokenization, embeddings, attention mechanism and masking techniques
  • Lora and qlora
  • quantization
• Challenges:
  • Adapting model to marketing-specific terminology
  • Balancing insight generation with relevance
• Outcomes:
  • Model generates actionable marketing insights
  • Identifies target audience and trends
  • Supports content creation decisions
• Business Impact:
  • Efficiency: automates insight generation, saving 10+ hours/week
  • Revenue growth: targeted content increases engagement by 20%
  • Data-driven decisions: actionable insights inform marketing strategies
• Explaining to Stakeholders:
  • "This model helps marketing teams extract valuable insights from data, identify trends, and target audiences more effectively."
  • "By automating insight generation, we can increase efficiency and drive revenue growth."