Building products

Every product that we use in AI / ML work seems to be built by a core set of a small number of focused Teams.

• Streamlit - 21 Employees
• BentoML - 8 Employees
• DagsHub - 13 Employees
• MLFlow - < 10 Employees

We need a focused team of passionate people. Employee count is just a number. Focus, Focus, Focus, Consistency, and Persistence. 

Keep Thinking!!!

Engineering Productivity Myth and Reality

This link was helpful in triggering the thoughts. The framework is interesting

From Work Perspective

• Design documents and specs
• Work items, pull requests, commits
• Code reviews/quality of reviews
• CI / CD - Count of build, test, deployment/release
• What I would also add up is
• Performance and scale while designing
• Field Testing to know it meets the customer

From General - work-life aspects

• Extensive and effective communication
• Block your peers to unblock you
• Lack of communication implies a lack of coordination

Mindset and culture play a key role

From ref

• Create a solution without understanding how it all works
• Continue creating a solution without understanding how it all works
• After something has caused you multiple issues, don’t stop and reconsider your approach

My perspectives - Ability to have quality ideas, quality alternatives in interest if time, Come back before its too late is essential.

The culture has to evolve (Ref)

• humble — self-aware, intellectually honest
• Know their business inside and out
• recruit dream people to the cause

Be clear about what you don't know and how you plan to achieve it

My perspectives - Quality ideas, quick experiments, balance time and don't end up half-baked product.

More reads

Link1, Link2, Link3, 1-1 discussions, Link4

Keep Thinking!!!