AWS Certification Training Courses

Cisco Certification Training Courses

CompTIA Certification Training Courses

EC-Council Certification Training Courses

Google Professional Certification Training Courses

HRCI Certification Training Courses

ISACA Certification Training Courses

ISC2 Certification Training Courses

Microsoft Certification Training Courses

NVIDIA Certification Training Courses

PeopleCert ITIL®4 Foundation Certification Training Course

Project Management Institute Training Courses

Scrum Alliance Certification Training Courses

ServiceNow Certification Training Courses

SHRM Certification Training Courses

VMware Certification Training Courses

Google Professional Machine Learning Engineer Certification Training Course

Course Overview

The Google Professional Machine Learning Engineer Certification Training Course by Accumentum is designed to empower cloud professionals with the skills required to excel in designing, building, and deploying machine learning models using Google Cloud’s advanced ML technologies. This course is tailored for individuals looking to strengthen their expertise in machine learning and data engineering, with a focus on creating scalable, efficient, and robust ML systems on Google Cloud. Participants will gain in-depth knowledge of machine learning workflows, including data preparation, model training, evaluation, and deployment, as well as the integration of AI solutions into production environments. The curriculum covers essential tools and practices such as TensorFlow, BigQuery ML, and AutoML, alongside strategies for optimizing model performance and ensuring scalability. Through hands-on labs and real-world case studies, learners will explore practical applications of Google Cloud’s ML capabilities, aligning technical proficiency with business objectives to deliver impactful AI-driven solutions. This training is ideal for data scientists, engineers, and IT professionals aiming to elevate their careers by mastering Google Cloud’s machine learning methodologies. It equips candidates not only to succeed in the certification exam but also to drive innovation and efficiency in their organizations through cutting-edge machine learning expertise.
Accumentum® | Expert IT Certification Training for Career Growth Accumentum® | Expert IT Certification Training for Career Growth

Course Objectives

  • Develop proficiency in designing, training, evaluating, and deploying machine learning models on Google Cloud, ensuring scalable and efficient solutions tailored to real-world applications.
  • Gain hands-on expertise with tools like TensorFlow, BigQuery ML, and AutoML to build, optimize, and productionize AI models that align with business needs.
  • Learn techniques to enhance model accuracy, scalability, and reliability, including hyperparameter tuning, feature engineering, and performance monitoring in production environments.
  • Acquire skills to seamlessly deploy and manage machine learning solutions, enabling organizations to drive innovation and achieve operational excellence through AI-driven insights.

Who Should Attend

  • Professionals seeking to advance their skills in designing and deploying machine learning models on Google Cloud for impactful, scalable AI solutions.
  • Individuals aiming to deepen their expertise in building, optimizing, and productionizing ML systems using Google’s cutting-edge tools and methodologies.
  • Those looking to expand their knowledge of machine learning integration within cloud environments to enhance operational efficiency and innovation.
  • Individuals preparing for the Google Professional Machine Learning Engineer certification who want practical, hands-on experience to excel in the exam and their careers.

Prerequisites

  • Familiarity with core machine learning concepts, such as supervised and unsupervised learning, model training, and evaluation metrics.
  • Proficiency in Python, including experience with libraries like TensorFlow or scikit-learn, to effectively implement and customize ML models.
  • Understanding of Google Cloud Platform basics, such as working with Compute Engine, Cloud Storage, or BigQuery, or completion of a foundational GCP course.
  • Experience with data preprocessing, feature engineering, and querying datasets (e.g., using SQL), to prepare and manage data for machine learning workflows.

Course Content

Introduction to Machine Learning on Google Cloud
  • Explore the role of machine learning in modern cloud architectures.
  • Understand Google Cloud’s ML ecosystem and its integration with business solutions.
  • Learn the fundamentals of the Google Professional Machine Learning Engineer certification.
  • Identify key use cases for ML deployment on Google Cloud.
Data Preparation and Feature Engineering
  • Master techniques for cleaning, transforming, and structuring raw data for ML models.
  • Use Google Cloud tools like Dataflow and BigQuery for efficient data preprocessing.
  • Implement feature selection and engineering to enhance model performance.
  • Handle missing data, outliers, and categorical variables effectively.
Machine Learning Model Design
  • Design supervised and unsupervised learning models tailored to specific problems.
  • Explore model architectures, including deep learning and ensemble methods.
  • Select appropriate algorithms based on data characteristics and business goals.
  • Understand trade-offs between model complexity, accuracy, and scalability.
Training and Tuning ML Models
  • Train models using Google Cloud’s AI Platform and Vertex AI.
  • Apply hyperparameter tuning with tools like AI Platform HyperTune.
  • Evaluate model performance using metrics like precision, recall, and AUC.
  • Mitigate overfitting and underfitting through regularization and validation techniques.
Model Deployment and Serving
  • Deploy trained models to production using Vertex AI endpoints.
  • Configure scalable, low-latency serving for real-time predictions.
  • Implement batch and online prediction workflows for diverse use cases.
  • Monitor deployed models for performance and drift in production environments.
Automation and Orchestration of ML Pipelines
  • Build end-to-end ML pipelines with Vertex AI Pipelines and Kubeflow.
  • Automate data ingestion, training, and deployment processes.
  • Integrate continuous integration/continuous deployment (CI/CD) for ML workflows.
  • Optimize pipeline efficiency to reduce latency and resource costs.
Scalability and Performance Optimization
  • Scale ML workloads using distributed training on Google Cloud infrastructure.
  • Optimize resource usage with preemptible VMs and custom accelerators (e.g., TPUs).
  • Implement caching and batching strategies for efficient inference.
  • Troubleshoot and resolve performance bottlenecks in large-scale ML systems.
Monitoring and Maintaining ML Systems
  • Set up monitoring with Cloud Monitoring to track model health and metrics.
  • Detect and address data drift, concept drift, and model degradation.
  • Use logging and alerting to ensure system reliability and uptime.
  • Establish retraining schedules to keep models current and accurate.
Advanced Tools and Frameworks
  • Leverage TensorFlow Extended (TFX) for production-ready ML pipelines.
  • Use AutoML to accelerate model development for rapid prototyping.
  • Explore BigQuery ML for training models directly on structured data.
  • Integrate custom models with Google Cloud’s pretrained APIs (e.g., Vision, NLP).
Security and Compliance in ML Systems
  • Implement data encryption and access controls to protect sensitive information.
  • Ensure compliance with regulations like GDPR and HIPAA in ML workflows.
  • Use Identity and Access Management (IAM) to secure Google Cloud resources.
  • Audit and validate ML systems for fairness, bias, and ethical considerations.

Course Features

Interactive Learning

Engage with expert instructors and peers through training sessions, discussions, and practical exercises.

Comprehensive Study Materials

Access extensive resources, including e-books, video lectures, and practice exams.

Real-World Applications

Work on real-life case studies and scenarios to apply Machine Learning Engineering concepts.

Certification Preparation

Receive guidance and tips to successfully pass the Google Professional Machine Learning Engineer certification exam.

Certification Exam

Upon completing the course, you will be thoroughly equipped to take the Google Professional Machine Learning Engineer Certification Exam. Earning this certification confirms your proficiency in machine learning practices, showcasing your ability to design, build, and deploy scalable, efficient, and robust ML systems on Google Cloud. This accomplishment will greatly enhance your career opportunities in machine learning and data engineering roles.

Enrollment

Join the Google Professional Machine Learning Engineer Certification Training Course at Accumentum to launch your path toward becoming a certified machine learning expert on Google Cloud. For more information and to register, visit Accumentum’s enrollment page linked below.

Registration

Accumentum® LLC

1550 Wilson Blvd

Suite 700

Arlington VA 22209

Copyright 2025 – Accumentum® LLC

All Rights Reserved. The certification names are trademarks of their owners.

X-twitter Linkedin