AI/ML Assessment
Evaluating artificial intelligence and machine learning capabilities
AI and ML assessments require specialized expertise to evaluate model quality, data dependencies, and operational maturity.
AI/ML Assessment Framework
1. Model Evaluation
- Model types and architectures
- Performance metrics and benchmarks
- Training data quality and provenance
- Model versioning and reproducibility
- Bias and fairness considerations
2. MLOps Maturity
- Model training pipelines
- Feature engineering processes
- Model deployment and serving
- Monitoring and retraining
- A/B testing capabilities
3. Data Dependencies
- Training data sources and rights
- Data labeling processes
- Data freshness requirements
- Third-party data dependencies
4. Team Capabilities
- Data science team composition
- Research vs. production focus
- Domain expertise
- Publication and patent history
AI/ML Red Flags
- Models trained on data the company doesn't own
- No model monitoring in production
- Single data scientist with all knowledge
- Models not retrained in 12+ months
- No bias testing or fairness evaluation
- Overstated AI capabilities in marketing
Valuation Considerations
- Is the AI truly differentiated or commodity?
- What's the moat around the data advantage?
- Can models be replicated with public data?
- What's the talent retention risk?
Key Takeaway: Many "AI companies" have limited actual AI. Validate claims with technical evidence and independent assessment.