Model training is 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. The purpose of model training is to build the best mathematical representation of the relationship between data features and a target label (in supervised learning [glossary link]) or among the features themselves (unsupervised learning [glossary link]). Loss functions [glossary link] are a critical aspect of model training since they define how to optimize the machine learning algorithms. Depending on the objective, type of data and algorithm, data science practitioner use different type of loss functions. One of the popular examples of loss functions is Mean Square Error (MSE) [glossary link].
Model training is the key step in machine learning that results in a model ready to be validated, tested, and deployed. The performance of the model determines the quality of the applications that are built using it. Quality of training data and the training algorithm are both important assets during the model training phase. Typically, training data is split for training, validation and testing. The training algorithm is chosen based on the end use case. There are a number of tradeoff points in deciding the best algorithm – model complexity, interpretability, performance, compute requirements, etc. All these aspects of model training make it both an involved and important process in the overall machine learning development cycle.
C3.ai enables distributed training through a mix of out–of–the–box and custom ML pipelines addressing different data science workload demands. The training of these pipelines creates ML models which can be analyzed in the C3 AI ML Studio, promoted for deployment, used for generating score reports, or evaluating model performance. Further these models could also be created using no–code drag–and–drop experiences provided by C3 AI Ex-Machina.
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