Trends
The process of training an AI model
Fundamentally, AI uses data to make predictions. That capability may power “you may also like” tips on streaming services, but it’s also behind chatbots capable of understanding natural language queries and predicting the correct answer and applications that look at a photo and use facial recognitio…
Headline
Fundamentally, AI uses data to make predictions. That capability may power “you may also like” tips on streaming services, but it’s also behind chatbots capable of understanding natural language queries and predicting the correct answer and applications that look at a photo and…
Context
Fundamentally, AI uses data to make predictions. That capability may power “you may also like” tips on streaming services, but it’s also behind chatbots capable of understanding natural language queries and predicting the correct answer and applications that look at a photo and use facial recognition to suggest who’s in the picture. Getting to those predictions, though, requires effective AI model training, and newer applications that depend on AI may demand slightly different approaches to learning. Successful AI model training starts with quality data that accurately and consistently represents real-world and authentic situations. Without it, ensuing results are meaningless. To succeed, project teams must curate the right data sources, build processes and infrastructure for manual and automated data collection, and institute appropriate cleaning/transformation processes.
Evidence
Pending intelligence enrichment.
Analysis
Also read: The 4 challenges of data management Also read: NLP techniques in data science If curating data provides the groundwork for the project, model selection builds the mechanism. Variables for this decision include defining project parameters and goals, choosing the architecture, and selecting model algorithms. Because different training models require different amounts of resources, these factors must be weighed against practical elements such as compute requirements, deadlines, costs, and complexity. Just as with the example above of teaching a child to tell a cat from a dog, AI model training starts with basics. Using too wide of a data set, too complex of an algorithm, or the wrong model type could lead to a system that simply processes data rather than learning and improving. During initial training, data scientists should focus on getting results within expected parameters while watching for algorithm-breaking mistakes. By training without overreaching, models can methodically improve in steady, assured steps.
Key Points
- Successful AI model training starts with quality data that accurately and consistently represents real-world and authentic situations.
- Using too wide of a data set, too complex of an algorithm, or the wrong model type could lead to a system that simply processes data rather than learning and improving.
Actions
Pending intelligence enrichment.
