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Who should take the Professional Machine Learning Engineer - Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

The Google Professional-Machine-Learning-Engineer exam is for entry-level IT specialists and organization professionals with standard knowledge of the Google platform. The Google CCP certification validates the potential client's understanding of these topics and their skills; standard building principles, key services and also their use cases, security, and protection, as well as compliance with the Google model, paid versions, and prices. Google Professional-Machine-Learning-Engineer exam is the appropriate starting point for Google certification and is also an excellent resource for those interested in non-technical projects.

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

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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:

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

Prerequisites

The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.

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