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Google Professional-Machine-Learning-Engineer: Professional Machine Learning Engineer

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Reading Time: 9 mins | Publish Date: 22 Jun 2021 | Update Date: 18 Nov 2021

How to Prepare For Professional Machine Learning Engineer - Google

Preparation Guide for Professional Machine Learning Engineer - Google

Introduction for 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 is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.

The Professional Machine Learning Engineer exam assesses your ability to:

  • Frame ML problems
  • Architect ML solutions
  • Prepare and process data
  • Develop ML models
  • Automate & orchestrate ML pipelines
  • Monitor, optimize, and maintain ML solutions

We prepare Google Professional-Machine-Learning-Engineer practice exams and Google Professional-Machine-Learning-Engineer practice tests to prepare you for all these requirements.

Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer dumps pdf will include the following topics:

  • ML Problem Framing
  • ML Solution Architecture
  • Data Preparation and Processing
  • ML Model Development
  • ML Pipeline Automation & Orchestration
  • ML Solution Monitoring, Optimization, and Maintenance

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 dumps:

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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture

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

  • Design reliable, scalable, highly available ML solution
  • Optimizing data use and storage
  • Data connections
  • Automation of data preparation and model training/deployment
  • SDLC best practices
  • Choose appropriate Google Cloud software components
  • A variety of component types - data collection; data management
  • Exploration/analysis
  • Feature engineering
  • Logging/management
  • Automation
  • Monitoring
  • Serving
  • Choose appropriate Google Cloud hardware components
  • Selection of quotas and compute/accelerators with components
  • Design architecture that complies with regulatory and security concerns
  • Building secure ML systems
  • Privacy implications of data usage
  • Identifying potential regulatory issues

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 dumps:

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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

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

  • Build a model
  • Choice of framework and model
  • Modeling techniques given interpretability requirements
  • Transfer learning
  • Model generalization
  • Overfitting
  • Productionizing
  • Training a model as a job in different environments
  • Tracking metrics during training
  • Retraining/redeployment evaluation
  • Unit tests for model training and serving
  • Model performance against baselines, simpler models, and across the time dimension
  • Model explainability on Cloud AI Platform
  • Scale model training and serving
  • Distributed training
  • Hardware accelerators
  • Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)

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 dumps:

Design pipeline. Considerations include:

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

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.

How to study the Professional Machine Learning Engineer - Google

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Professional Machine Learning Engineer - Google Certification Path

The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.

Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.

How much Professional Machine Learning Engineer - Google Cost

The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.

How to book the Professional Machine Learning Engineer - Google

To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:

  • Step 1: Go to the Google Official Site
  • Step 2: Read the instruction carefully
  • Step 3: Follow the given steps
  • Step 4: Apply for the Professional Machine Learning Engineer Exam

What is the duration, language, and format of Professional Machine Learning Engineer - Google

  • Duration of Exam: 120 minutes
  • No negative marking for wrong answers
  • Type of Questions: Multiple choice (MCQs), multiple answers
  • Language of Exam: English, Japanese, Korean

Professional Machine Learning Engineer - Google Certified salary

The estimated average salary of Professional Machine Learning Engineer - Google is listed below:

  • United States: 114,000 USD
  • India: 8,580,000 INR
  • Europe: 97,000 EURO
  • England: 87,200 POUND

The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

  • 87% of Google Cloud certified individuals are more confident about their cloud skills
  • Professional Cloud Architect was the highest paying certification of 2020 and 2019
  • More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work

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