A model registry is used to manage model versions and metadata, ensuring that different versions of models can be tracked and accessed during deployment.

A feature store is used to manage and serve features for machine learning models, ensuring consistency and reusability.

A load balancer distributes incoming requests across multiple instances of a model to ensure efficient resource utilization and high availability.

A model registry helps manage different versions of models and their associated metadata, facilitating easier deployment and tracking.

A model serving framework primarily manages model versions and scaling, ensuring that the model can handle requests efficiently in production.

A model serving framework is designed to manage and serve machine learning models in production, facilitating their use in applications.

A REST API serves predictions from the deployed model, allowing applications to interact with it.

The validation set is used to tune hyperparameters and assess the model's performance during training, helping to prevent overfitting.

An API allows external applications to interact with the deployed model, enabling them to send data and receive predictions.

APIs (Application Programming Interfaces) allow external applications to interact with the deployed model, enabling predictions and data exchange.

AutoML simplifies the model training process by automating tasks such as feature selection and hyperparameter tuning.

Backpropagation is an algorithm used to update the weights of the neural network based on the error calculated from the output.

In bioinformatics, clustering is used to group similar genes or proteins based on their expression patterns.

Containerization helps package the model along with its dependencies, ensuring consistent deployment across different environments.

Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of input units to zero during training.

Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of input units to zero during training.

Feature engineering plays a crucial role in enhancing model performance by creating new features or modifying existing ones to better represent the underlying data.

Feature importance helps identify which features are most influential in making predictions, aiding in feature selection and model interpretation.

Feature scaling ensures that all features contribute equally to the model by standardizing or normalizing their values.

Hyperparameter tuning is crucial for optimizing model performance by finding the best set of parameters for the chosen algorithm.

Monitoring is essential to track the performance of deployed models and detect any issues that may arise over time.

Monitoring is crucial in model deployment as it helps track model performance and detect any issues that may arise after deployment.

Regularization adds a penalty for larger coefficients in the model, helping to prevent overfitting and improve generalization.

The 'k' parameter in K-means specifies the number of clusters that the algorithm will attempt to form from the data.

The 'max_depth' parameter limits how deep the Decision Tree can grow, helping to prevent overfitting.

The 'max_depth' parameter limits how deep the tree can grow, helping to prevent overfitting by controlling model complexity.

'max_features' specifies the maximum number of features to consider when looking for the best split, which helps to introduce randomness and reduce correlation among trees.

The fully connected layer connects every neuron in one layer to every neuron in the next layer, allowing for integration of features learned by previous layers.

Hidden layers perform computations and extract features from the input data, enabling the network to learn complex representations.

'C' is a regularization parameter that controls the trade-off between maximizing the margin and minimizing the classification error.