A. Leveraging specific functionalities for data analysis, manipulation, and model building
B. Deploying models that are incompatible with the deployment environment
C. Increasing the complexity and maintenance cost of the deployed solution
D. Ensuring that all models are developed without any external libraries
A. Deploying all models simultaneously regardless of use case
B. The ability to update or retire models based on performance metrics
C. Avoiding the use of APIs for integration with applications
D. Ensuring there is no logging or monitoring of model performance
A. To ensure the model uses all available computational resources
B. To increase the number of features in the dataset automatically
C. To reduce the training time of the model to an absolute minimum
D. To adjust the model's complexity to improve its performance on unseen data
A. When computational resources are limited and model interpretability is not a concern.
B. When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
C. When the dataset is small and easily interpretable.
D. For simple tasks that require straightforward predictive modeling.
A. You need a balance between precision and recall.
B. Only the model's accuracy matters.
C. The dataset size is extremely large.
D. The data is completely balanced.
A. Transforming data exclusively in cloud environments
B. Loading data into a single, centralized database for analysis
C. Extracting the least amount of data for simplicity
D. Ensuring data quality and consistency throughout the process
A. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
B. Building models and using their output as features into a final model.
C. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
D. Building models in parallel and aggregating their predictions to select the final prediction.
A. Real-time performance monitoring
B. Version control of deployed models
C. Automatic conversion of all models to deep learning models
D. Rollback capabilities for model versions
