Often Missing SkillsMachine Learning Lifecycle KnowledgeModel Monitoring ConceptsData Privacy BasicsExperimentation MethodsMetrics and MeasurementTechnical Writing
Development SuggestionsBuild a practical understanding of how models are trained, tested, launched, and monitored. Practice writing clear one page plans, defining success metrics, and running structured reviews for risks, privacy, and readiness. Partner closely with data science and engineering to learn common failure modes and operational needs.