An impactful ML Model — how hard can it be?

  1. If there is enough data to make a model that can indeed increase personalization by 5%
  2. Investment needed to get that model built and deployed which is providing that impact on a continual basis.

Show the estimated delivery time to user for a food delivery app

Project Ideation

Data Gathering

  • In some cases, previous data might not be logged properly — log the data and collect it first (Product Team, Data Engineering Team).
  • In some fortunate cases this data might be easily available
  • In many cases, this will require ETL pipelines to be written to get the data in the right format. The Data Engineering team will write the pipelines to get the data in the required format.

Data Analysis

Feature Engineering

Model Training

Log metrics, params, models during training and share with team

Model Serving

Product Integration

Model Monitoring

  1. System Monitoring: This includes metrics like cpu, memory, api latency, errors, crashes of the model and usually done using Prometheus / Grafana or paid solutions like Datadog / New Relic. This will be used by Engineering, Product and the Datascience team.

Complete Automation

Evaluation of Business Impact:

  1. Product Manager / Business team
  2. Data Engineering Team
  3. Data Science Team
  4. ML Engineering Team
  5. Backend Engineering Team
  6. Devops Team



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Abhishek Choudhary

Abhishek Choudhary

Enterprenuer | Ex-Facebook Hacker | Travel | Musician by aspirations