The essence of artificial intelligence lies in data. If we don't have sufficient data, we cannot train the models to do what we want, and our expensive and powerful hardware becomes useless. But procuring relevant, accurate, legitimate, and reliable data is easier said than done. That's because the data collection process is often just as complex and time-intensive as setting up the actual machine learning models. Moreover, it also takes time and considerable resources to collect, document, and clean data before it can be used.

Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn. ML models can be trained to benefit businesses in numerous ways, by quickly processing huge volumes of data, identifying patterns, finding anomalies or testing correlations that would be difficult for a human to do unaided. Model training is at the heart of the data science development lifecycle where the data science team works to fit the best weights and biases to an algorithm to minimize the loss function over prediction range. Loss functions define how to optimize the ML algorithms. A data science team may use different types of loss functions depending on the project objectives, the type of data used and the type of algorithm.

Tensorflow Serving uses gRPC protocol and we need to create a client to issue inference requests.