Gated Recurrent Units, colloquially known as GRUs, are a significant aspect of Recurrent Neural Networks (RNNs), a class of artificial neural networks that remain virtually paramount in the world of deep learning. A genre of RNN tailored towards effectively handling sequential data with long-term dependencies, GRUs offer invaluable solutions typically employed in various fields, particularly those involving natural language processing and speech recognition.

Designed to solve the vanishing gradient problem found in traditional RNNs, GRUs provide us with specific operational features:

• Temporal Dependency: GRUs make use of different gates to modulate the flow of information among units, effectively handling both short and long-term dependencies.
• Updation Strategy: These units employ an 'update gate' and a 'reset gate'. The former determines the extent of past information to carry forward, while the latter decides on the degree of excluding existing state information.
• Activation Function: Gated Recurrent Units prominently utilize the 'tanh' and 'sigmoid' functions. The former serves to normalize the output, keeping it within a range of -1 to 1, while the latter aids in making binary decisions.
• Parametrization: Parametrization is less than LSTM (other RNNs), yet it's able to match its performance, enhancing the execution time while reducing the computational strain.

While venturing to integrate GRUs into deep learning models, it's essential to understand the unique requirements and compatibility of the dataset and the environment in which it is to function. Pre-processing the data, setting the model parameters correctly and defining the appropriate architecture are steps that should follow a comprehensive analysis. Regular monitoring of the model throughout training and making necessary adjustments ensures maximum performance.

GRUs represent a milestone in deep learning, making sequence prediction more accurate and efficient. Despite having certain limitations, their widespread applicability and robust performance make them an essential tool in the domain of artificial intelligence and machine learning. An understanding of their benefits and limitation paves the way for discerning choice, ultimately harnessing their true potential.

Numerous beneficial aspects get manifested in GRUs, triggering their preference across multiple fields:

• Effective Memory Handling: GRUs have an edge in managing memory-related tasks due to their inherent model simplicity. They can regulate and modulate the carried forward data effectively, addressing both long- and short-term dependencies.
• Computational Efficiency: GRUs tend to provide comparable results to their LSTM counterparts while exhibiting a lower computational complexity, making them an attractive choice in many applications where runtime performance is essential.
• Training Efficiency: As GRUs have fewer parameters to be trained compared to LSTMs, their learning process is more efficient and stable, favoring quicker network convergence.
• Flexibility: GRUs are remarkably adaptable across an array of applications, Credit goes to their gate mechanisms enabling them to recognize and catch dependencies at varying time scales.

Like every technology, GRUs also showcase certain limitations:

• Increased Complexity: Regardless of its advantage over traditional RNNs, GRUs still exhibit increased complexity compared to feed-forward and convolutional networks due to gate mechanisms and recurring architecture.
• Dependency on Data: GRUs' effectiveness heavily hinges upon the data quality. Insufficient or misaligned data can impact the performance and skew the results.
• Resource Intensive: GRUs need substantial computational resources for training, and may require powerful hardware to handle large datasets.
• Limited Interpretability: As all other types of recurrent neural networks, GRUs lack immediate explainability due to their inherent complexity.