ML Lessons from Production Implementation

Good Article Link. The summary is very good

For each lesson, I have added my personal observations for few points.

1. Subject matter experts have as much impact as data scientists

• Fact - "much of the challenge is getting the right data."
• Add-on - "much of the challenge is getting the right data and creating right insights / correct observations / Finding hidden patterns with domain knowledge / look beyond data what drives it"

2. The first iteration is always on the labeling taxonomy - "In vision projects having right labeled data becomes essential for detection, extraction, analysis etc.."

3. The ROI on fast feedback is huge - rapid prototyping and de-risking of projects. - "People lose confidence without seeing the value realization. Getting business involved early and understand their KPI, measure to analyze the impact of ML solution is key for the success of the project"

4. ML tools should be data-centric but model-backed - "It's a tradeoff to learn domain vs ML vs DevOps vs New tools in markets. Often end customers do not see ML as a standalone item, They get together with their existing data warehouse, You need to be practical to pick the tools which make it less complicated to integrate the current environment build a successful use case."

#datascience #analytics #domainknowledge

Keep Thinking!!!