Accuracy	It is the number of correct predictions divided by the total number of predictions made. Accuracy highlights the rate of accurate model prediction.
Accuracy Metrics	Accuracy metrics indicate numerical values that evaluate the performance of the model.
Data validation	It is to check the accuracy and quality of the source data before training. It ensures that the mistakes are addressed and not silently ignored.
Dataset	A set of data available in a tabular format is called a dataset. It is the set of data processed in a machine to make the predictions.
F1 score	It is the harmonic mean of precision and recall, taking both metrics into account.
Machine Learning Operations (MLOps)	MLOps is a set of best practices that seamlessly brings data science solutions into existing systems. Enterprises can set up efficient data science solutions without disturbing their current set up.
Machine learning model	A machine learning model is a mathematical representation of a set of data and is used to make predictions/inferences. There are several types of machine learning models available and the models offer unique advantages for different use cases.
Model training	A data science model needs to be trained with available data to make effective predictions on newer data. A machine learning algorithm learns from a dataset and predicts results for a different set of data.
Precision	The ability of a classification model to identify only the relevant data points.
Re-training	Any model will decay as time elapses and new data keeps coming in. Consequently, deployed models would need retraining at certain intervals. Re-training with newer data increases the prediction accuracy in the longer run.
Recall	The ability of a model to find all the relevant cases within a dataset.