"Mastering ML OPS for Image Models"
Model Testing for Image ModelsModel testing for image models involves evaluating the performance of the model in terms of accuracy, precision, recall, and F1 score. This can be done by splitting the dataset into training and testing sets, and using the testing set to evaluate the model's performance. Additionally, cross-validation can be used to ensure that the model is not overfitting to the training data. Exploratory Data Analysis (EDA) and Testing for Image ModelsEDA for image models involves visualizing the data to gain insights into the distribution of the images, the presence of outliers, and the relationships between the features and the target variable. Testing for image models involves checking for class imbalance, data augmentation, and preprocessing techniques such as normalization and resizing. Skills Required for ML OPS for Image ModelsML OPS for image models requires a strong understanding of computer vision techniques, deep learning frameworks such as TensorFlow and PyTorch, and cloud computing platforms such as AWS and Azure. Additionally, skills in data engineering, DevOps, and software development are necessary to deploy and maintain the model in production. |