<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["de", "en"].<end_of_turn>
<start_of_turn>user
Welche Rolle spielt ein Machine-Learning-Ingenieur bei der Datenvorverarbeitung?<end_of_turn>
<start_of_turn>model
A Machine Learning Engineer plays a crucial role in data preprocessing, which is an essential step in the machine learning pipeline. Here are the key responsibilities of a Machine Learning Engineer in data preprocessing:

Data Collection and Integration: They are responsible for gathering data from various sources, which may include databases, online repositories, or real-time data streams. This also involves integrating disparate data formats into a consistent and usable format.

Data Cleaning: This involves handling missing values, correcting errors, and removing duplicates. Machine Learning Engineers ensure the quality and accuracy of data, as dirty or incorrect data can lead to unreliable models and predictions.

Data Transformation: This includes normalizing or scaling data to ensure that numeric features have the same scale, which is important for many machine learning algorithms. They may also apply transformations like log transformation to reduce skewness in the data.

Feature Engineering: They create new features from existing data to improve model performance. This can involve extracting information from text data, creating interaction terms, or breaking down dates and times into components.

Data Reduction: Machine Learning Engineers may apply techniques like dimensionality reduction to reduce the number of features in the dataset, which can help in improving model performance and reducing computational cost.

Handling Imbalanced Data: In cases where there are uneven samples of different classes, they might use techniques such as oversampling the minority class or undersampling the majority class to ensure a balanced dataset.

Data Splitting: They divide the dataset into training, validation, and test sets to ensure robust evaluation and to prevent overfitting.

Ensuring Data Privacy and Security: When preprocessing sensitive data, they must ensure that privacy and security standards are met, including compliance with data protection regulations.

Pipeline Development: They often develop automated data preprocessing pipelines which can efficiently process new data as it becomes available.

Exploratory Data Analysis (EDA): Although typically associated with Data Scientists, Machine Learning Engineers may also engage in EDA to understand the distributions, relationships, and potential issues in the data.

By effectively handling these aspects of data preprocessing, Machine Learning Engineers lay the groundwork for building robust, efficient, and accurate machine learning models.<end_of_turn><eos>