Data - Use Case - Iterative Thinking - Evolving solutions

Many times learning comes from people around us. For vehicle PPF it gave a lot of insights

• While removing all the door cladding/mirrors, the 0-2yrs exp people were putting all screws together
• While fitting on screws, Same dimension screws were applied for two parts, There was confusion on pending screws
• The PPF person was very focused, and the sun film person was a separate person

Three things to build a solution

Many times learning comes from people around us. For vehicle PPF it gave a lot of insights

Observation #1

• Fresher Lens -  While removing all the door cladding/mirrors, the 0-2yrs exp people were putting all screws together
• Experience Lens  - The person (lead) he asked them group based on the parts
• Lesson - group problems, data logically to debug / build solution

Observation #2

• Fresher Lens - While fitting on screws, Same dimension screws were applied for two parts One screw was perfect silver, and another was perfect black, When all screws were filled, juniors were clueless about where it had to be fitted
• Experience Lens  - The lead was able to provide clarity to fit the connecting dots
• Lesson - Leadership is solving with what you have, not afraid to look back and rework where it is needed

Observation #3

• The PPF person was very focused, the sun film person was a separate person
• Lesson - Build relationships, cannot solve all problems all alone
• Be good at a few, use expertise when you need a great product

What does it imply in the AIML context

• Inexperienced team members initially consolidated all components indiscriminately during disassembly. The team leader guided them to categorize components systematically, akin to structuring data for efficient algorithmic problem-solving.
• Different screws of identical size were used interchangeably during the assembly process, leading to confusion. The leader demonstrated critical thinking, using available resources to retrospectively address the issue, a trait essential for refining machine learning models.
• Task specialization was evident; individuals focused on PPF or sun film application roles. This mirrors the need for specialization and collaboration in AIML, leveraging cross-disciplinary expertise to enhance overall model performance.

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