Scaling AI solutions in growing companies

[Joshua Medel]

It’s one thing to build a model, but another to scale it. What challenges appear when AI solutions move from prototype to production in real businesses?

[Greten]

Scaling AI solutions introduces new challenges that don’t appear at the prototype stage. Infrastructure нагрузка, model monitoring, and continuous retraining become critical. What worked in a small test may fail under real user traffic. That’s why it’s important to combine engineering and analytics from the beginning. Some companies follow integrated approaches like those described here — Data Science UA, where both data pipelines and models are designed with scalability in mind.