Adding ML the practical way: recommendations & forecasting

RR InfoTek Data
RR InfoTek Data
Data & AI
2025-03-01
  • machine-learning
  • ai
  • analytics
Adding ML the practical way: recommendations & forecasting

Start small, measure impact, then productionize with feature stores, drift checks, and observability.

In this post we share the patterns, trade-offs, and checklists we actually use on client projects. The goal is production readiness from day one—consistent environments, fast feedback cycles, clean deployment stories, and observability built in.

What you’ll learn

  • Structure that scales (code, environments, and releases)
  • Common pitfalls and how we avoid them
  • How we automate testing, linting, and shipping

We’ll keep this practical with real examples you can copy/paste and adapt for your team.