- 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.
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.
Prepare the data
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.
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Select a training model
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.
Perform initial training
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.
Validate the training
Once the model passes the initial training phase, it reliably creates expected results across key criteria. Training validation represents the next phase. Here, experts set out to appropriately challenge the model in an effort to reveal problems, surprises, or gaps in the algorithm. This stage uses a separate group of data sets from the initial phase, generally with increased breadth and complexity versus the training data sets.
As data scientists run passes with these data sets, they evaluate the model’s performance. While output accuracy is important, the process itself is just as critical. Top priorities for the process include variables such as precision, the percentage of accurate predictions, and recall, the percentage of correct class identification. In some cases, the results can be judged with a metric value. For example, an F1 score is a metric assigned to classification models that incorporate the weights of different types of false positives/negatives, allowing a more holistic interpretation of the model’s success.
Test the model
Once the model has been validated using curated and fit-for-purpose data sets, live data can be used to test performance and accuracy. The data sets for this stage should be pulled from real-world scenarios, a proverbial “taking the training wheels off” step to let the model fly on its own. If the model delivers accurate—and more importantly, expected—results with test data, it’s ready to go live. If the model shows deficiencies in any way, the training process repeats until the model meets or exceeds performance standards.
