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

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 ML Problem Framing

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

Determination of when a model is deemed unsuccessful
Assessing ML solution readiness
Define business success criteria
Defining the input (features) and predicted output format
Identifying nonML solutions
Defining outcome of model predictions
Defining business problems
Aligning with Google AI principles and practices (e.g. different biases)
Success metrics
Key results
Managing incorrect results
Defining problem type (classification, regression, clustering, etc.)
Identify risks to feasibility and implementation of ML solution. Considerations include:
Defining output use
Assessing and communicating business impact
Define ML problem
Assessing data readiness
Identifying data sources

Exam Topics

The successful performance in the Google Professional Machine Learning Engineer certification test requires a good comprehension of its topics. The exam syllabus consists of six sections that are described below:

Architecting Machine Learning Solutions
Here the examinees need to demonstrate their proficiency in designing reliable, scalable, and highly available Machine Learning solutions. Besides that, the test takers need to be capable of selecting the proper Google Cloud hardware components, including evaluating accelerator and compute options (for example, CPU, TPU, GPU, edge devices). Lastly, they need to have the expertise in designing an architecture that meets the security concerns across the industries/sectors.
Framing Problems Related to Machine Learning
Within this subject area, the candidates should be capable of translating business challenges into the Machine Learning use cases. They should also possess the skills in determining the Machine Learning problems, identifying the business success criteria, as well as defining risks to the feasibility of the Machine Learning solutions.
Automating & Orchestrating Machine Learning Pipelines
This module encompasses one’s competency in designing & implementing training pipelines. This includes your ability to define the components, triggers, parameters, and compute needs; understanding of the orchestration framework; familiarity with the multi-Cloud or hybrid strategies; knowledge of system design involving the TFX components/Kubeflow DSL. The candidates should also possess the skills in implementing serving pipelines, including serving (online, caching, batch), testing for target performance, configuring trigger & pipeline schedules, among other skills. Apart from that, this part requires the students’ expertise in tracking & auditing metadata.
Monitoring, Optimizing, and Maintaining Machine Learning Solutions
This objective evaluates the competency of the applicants in monitoring and troubleshooting the Machine Learning solutions. The individuals should also be able to tune the performance of Machine Learning for training and serving in production. This involves the ability to optimize and simplify the input pipeline for training as well as knowledge of the simplification techniques.
Designing Data Preparation & Processing Systems
The aim of this topic is to measure the individuals’ skills in exploring data (Exploratory Data Analysis). This involves their understanding of visualization, statistical fundamentals at scale, data quality & feasibility evaluation, as well as data constraint establishment. It also evaluates the ability of the test takers to build data pipelines, in particular, organize and optimize training datasets, validate data, handle missing data, handle outliers, etc. You should also know how to create the input features (feature engineering). This envisages the familiarity with encoding structured data types, feature selection, class imbalance, feature crosses, transformations, and more.
Developing Machine Learning Models
To answer the questions related to this section, the learners should know how to build, test, and train models. They should also possess the skills in scaling model training as well as serving, including distributed training and scaling prediction service (for instance, containerized serving, AI Platform Prediction, etc.).

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

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