model training
What It Means
Model training is the process where an AI system learns patterns from your data by adjusting its internal parameters millions of times until it can make accurate predictions. Think of it like teaching a new employee by showing them thousands of examples until they can handle similar situations on their own. The system automatically fine-tunes itself based on feedback about whether its predictions were right or wrong.
Why Chief AI Officers Care
Training costs can consume 60-80% of your AI project budget through compute resources, data preparation, and engineering time, making it a major financial decision. Poor training practices lead to biased models, regulatory violations, and systems that fail in production, potentially costing millions in remediation and lost business. The quality of training directly determines whether your AI delivers ROI or becomes a expensive liability.
Real-World Example
A retail bank training a fraud detection model feeds it 2 million historical transactions labeled as legitimate or fraudulent. The system processes this data repeatedly over several days, adjusting its decision-making rules until it can correctly identify 95% of fraudulent transactions while minimizing false alarms that would block legitimate customer purchases.
Common Confusion
People often think training is a one-time event like installing software, but it's actually an iterative process that may need to be repeated as business conditions change or data quality issues emerge. Training is also frequently confused with deployment - training creates the model, but the model still needs separate implementation to actually start making business decisions.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, model training involves feeding AI systems vast amounts of medical data—such as imaging scans, patient ...
Finance: In finance, model training involves feeding historical market data, transaction records, and economic indicators to AI s...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"the phase in the data science development lifecycle where practitioners try to fit the best combination of weights and bias to a machine learning algorithm to minimize a loss function over the prediction range"Source: C3.ai_Model_Training
"process to determine or to improve the parameters of a machine learning model, based on a machine learning algorithm, by using training data"Source: aime_measurement_2022, citing ISO/IEC 22989
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