The Process of Training an AI Model

Although each project has its own challenges and requirements, the general process of training an AI model remains unchanged.

These five steps form an overview of AI model training.

1. Prepare the Data: Successful AI model training starts with high-quality data that must accurately and consistently represent real-world situations. Without this data, the subsequent results will be meaningless. To succeed, the project team must carefully select appropriate data sources, build processes and infrastructure for both manual and automated data collection, and establish suitable cleaning/transformation workflows.

2. Choose the Training Model: If data organization lays the foundation for the project, then model selection builds the entire mechanism. Variables in this decision include defining project parameters and goals, choosing the architecture, and selecting the model algorithm. Since different training models require varying amounts of resources, these factors must be weighed against practical considerations such as computational demands, deadlines, costs, and complexity.

3. Initial Training: Just like the example of teaching a child to distinguish between cats and dogs, AI model training begins with the basics. Using an overly broad dataset, overly complex algorithms, or the wrong model type can lead to the system merely processing data, not learning and improving. During the initial training phase, data scientists should focus on obtaining results that align with expected parameters while watching for potentially destructive errors in the algorithm. With careful training, the model can improve progressively, steadily, and confidently.

4. Validate the Training: After the initial training phase, the model can reliably create expected results based on key criteria. The next phase is training validation. In this stage, experts begin to appropriately challenge the model to uncover issues, surprises, or gaps in the algorithm. This phase uses a different dataset than the initial training phase, usually with greater breadth and complexity than the training dataset.

When data scientists test using these datasets, they evaluate the model's performance. While output accuracy is important, the evaluation process itself is equally critical. Key tasks in this process include variables like accuracy (the percentage of correct predictions) and recall (the percentage of correct category recognitions). In some cases, a single metric value can be used to judge the results. For example, the F1 score is a metric assigned to classification models, which considers the weight of different types of false positives/false negatives, providing a more comprehensive view of the model's success.

1. Test the Model: After validating the model with selected, applicable datasets, it is tested using real-time data to evaluate its performance and accuracy. This phase's datasets should come from real-world scenarios and is referred to as the "out-of-training-loop" step, aimed at letting the model develop on its own. If the model can deliver accurate results (and more importantly, meet expected outcomes) with test data, it is ready for deployment. If the model has any defects, the training process must be repeated until it meets or exceeds performance standards.

While going live is an important milestone, it doesn't mean the end of model training. Depending on the model, each processed dataset can become another "lesson" for the AI, further improving and refining the algorithm. Data scientists must continuously monitor performance and results, especially when the model processes unexpected outlier data. If any inaccuracies appear, even in small amounts, adjustments may be required to prevent future output from being negatively affected.