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:

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

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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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
ML model development	- Evaluation 1. Model validation strategies 2. Evaluate model performance metrics - Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI
ML pipeline automation and orchestration	- Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines
Designing ML solutions	- Framing ML problems 1. Translate business problems into ML tasks 2. Define success metrics and evaluation criteria - ML architecture design 1. Select appropriate ML models and approaches 2. Design scalable ML systems on GCP
Data preparation and processing	- Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques - Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services
Deployment and operations	- Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems - Monitoring and maintenance 1. Retraining and lifecycle management 2. Monitor model drift and performance

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