Inspiration: Tesla employs differential privacy when collecting vehicle telemetry for autonomous driving improvements

What it does: Helps users diagnose common mechanical system failures based on symptoms and suggests causes + remedies.

How we built it: Use model from groq and deploy it on hugging face. Make a chat bot for model fine tuning, use streamlit for interface , two files to deploy app.py and requirements.txt

Challenges we ran into: Mishandled AI training data can lead to financial penalties, reputational damage, and loss of customer trust in today's regulatory environment.

Accomplishments that we're proud of: heck oil levels, inspect gear wear, and much more on diagnostics

What we learned: Data security is essential 4 not optional, Ethical handling of data builds trust, Proactive security drives innovation

What's next for Failure Diagnosis Bot – Machine Fault Analyzer: create competitive advantage, Security efforts, building strong data, security habits today

Built With

  • googlecolab
  • groq
  • huggingface
  • streamlit
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