You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?

AutoML platforms automate algorithm selection, feature engineering, and hyper-parameter tuning, producing competitive models when skilled data scientists are unavailable. CPMAI highlights AutoML as a practical mitigation for teams with limited ML expertise and tight timelines.

A. Outsourcing adds cost, IP risk, and delays; CPMAI suggests in-house automation first.
B. Citizen data scientists still need tooling; AutoML directly fills the skill gap.
C. Pausing forfeits project value and violates CPMAI time-to-value guidance.

1. Cognilytica. CPMAI v7 Methodology Guide, Phase 4, p. 54 (“AutoML reduces dependence on scarce data-science talent”).

2. Google Cloud. “AutoML Tables Whitepaper,” v1, 2020, §1.2 (“automates model selection & tuning”).

3. MIT 6.036 “Introduction to Machine Learning,” Recitation 12 slides, 2021 (“AutoML for teams lacking ML experts”).