Machine learning (ML) has become ubiquitous. Our customers are employing ML in every aspect of their business, including the products and services they build, and for drawing insights about their customers.
To build an ML-based application, you have to first build the ML model that serves your business requirement. Building ML models involves preparing the data for training, extracting features, and then training and fine-tuning the model using the features. Next, the model has to be put to work so that it can generate inference (or predictions) from new data, which can then be used in the application. Although you can integrate the model directly into an application, the approach that works well for production-grade applications is to deploy the model behind an endpoint and then invoke the endpoint via a RESTful API call to obtain the inference. In this approach, the model is typically deployed on an infrastructure (compute,

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