Glossary
AI Training
AI training refers to the process of teaching machine learning models to perform tasks by feeding them large amounts of relevant data. The model analyzes this data to recognize patterns and make predictions. Training is critical to the success of AI systems, as it determines how accurately they can perform their intended functions.
What You Need to Know
AI models do not inherently know how to solve problems. They require structured training to learn from examples. For example, a model designed to predict customer churn needs data on past customer behavior, including which customers left and which stayed. The model uses this data to identify trends and generate predictions about future customer retention.
Training often occurs in phases. Initially, the model trains on a dataset where the correct outcomes are known. This is called supervised learning. The model’s performance improves through repeated exposure to the data and continuous fine-tuning of its internal algorithms. Once trained, the model can analyze new data and make accurate predictions.
How It Works
The AI training process involves several key steps:
- Data Collection: The model needs a comprehensive dataset that includes relevant features for the task.
- Data Labeling: In supervised learning, each data point must have an associated label (e.g., whether a customer churned or not).
- Model Training: The model analyzes the labeled data and adjusts its parameters to reduce errors.
- Evaluation: After training, the model is tested on a separate dataset to measure its accuracy.
- Iteration: If the performance is unsatisfactory, the training process is repeated with refined data or adjusted parameters.