Building ML Teams - liniribeiro/machine_learning GitHub Wiki

Vision

To build a multidisciplinary and autonomous Machine Learning team capable of researching, developing, validating, and operating models in production with a direct impact on the business. The goal is to turn data into intelligent, scalable, and efficient decisions and automations.

Goals

  1. Reduce time-to-market for ML-based solutions.
  2. Increase the success rate of models in production, avoiding models that never leave the research stage.
  3. Develop robust, auditable, and scalable solutions that integrate well with the current technical ecosystem.
  4. Enable the ML team to operate independently, avoiding constant reliance on backend or infrastructure teams.

Challenges

Lack of ownership over inference pipelines, monitoring, and experimentation. High dependency on other teams for deployments, adjustments, and operational support.

Roles and Responsibilities

Data Scientists

  • Explores data, builds and validates models.
  • Collaborates with product teams to define hypotheses.
  • Evaluates both business and model performance metrics.

ML Engineer / MLOps Engineer

  • Turns prototypes into robust production pipelines.
  • Handles performance, versioning, deployment, and scalability of models.
  • Bridges the gap between research and engineering.
  • Automates CI/CD for models.
  • Implements monitoring, testing, and retraining pipelines.
  • Ensures quality, traceability, and reproducibility of models in production.

(Optional) Product manager

  • Connects product vision with technical feasibility.
  • Defines success metrics for ML initiatives.
  • Prioritizes impactful ML deliveries for the business.

Benefits

  • Speed: Full cycle from research to production handled within the team.
  • Quality: Continuous testing and monitoring ensure model health.
  • Scalability: New models can be deployed and maintained with less friction.
  • Autonomy: The team solves end-to-end problems independently.
  • Business Alignment: Models are developed with clear impact on business outcomes.