Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Design data pipelines
• Class imbalance
• Monitoring/changing deployed pipelines
• Build data pipelines
• Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
• Data exploration (EDA)
• Evaluation of data quality and feasibility
• Feature crosses
• Streaming data (e.g. from IoT devices)
• Database migration
• Data leakage and augmentation
• Data ingestion
• Batching and streaming data pipelines at scale
• Data validation
• Feature engineering
• Visualization
• Feature selection
• Handling missing data
• Handling outliers
• Encoding structured data types
• Data privacy and compliance
• Transformations (TensorFlow Transform)
• Statistical fundamentals at scale
• Managing large samples (TFRecords)

The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

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Preparation Process

The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.

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