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.

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

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.

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:

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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration

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

Design pipeline. Considerations include:

• Model binary options
• Implement training pipeline
• Decoupling components with Cloud Build
• Track and audit metadata
• Model/dataset lineage
• A/B and canary testing
• Hooking into model and dataset versioning
• Testing for target performance
• Setup of trigger and pipeline schedule
• Hooking models into existing CI/CD deployment system
• Use CI/CD to test and deploy models
• Constructing and testing of parameterized pipeline definition in SDK
• Storing data and generated artifacts
• Identification of components, parameters, triggers, and compute needs
• Implement serving pipeline
• Hybrid or multi-cloud strategies
• Performing data validation
• Tuning compute performance
• Orchestration framework
• Google Cloud serving options
• Organization and tracking experiments and pipeline runs