Google Professional Machine Learning Engineer

Master Machine Learning Skills for Real-World Impact

Master the core skills needed to excel as a Machine Learning Engineer with this in-depth course. Tailored to prepare you for the Google Cloud Professional Machine Learning Engineer certification exam, this program empowers you to design, build, and deploy effective ML models that solve real-world business problems using Google Cloud technologies.

Gain practical insights into production-grade ML workflows, industry best practices, and how this certification can accelerate your career in AI and data science.

Google Professional Machine Learning Engineer

Module 1: Introduction to AI and Machine Learning on Google Cloud

• AI Foundations on Google Cloud
• AI Development on Google Cloud
• ML Workflow and Vertex AI
• Generative AI on Google Cloud

Module 2: Prepare Data for ML APIs on Google Cloud

• Vertex AI: Qwik Start
• Dataprep: Qwik Start
• Dataflow: Qwik Start – Templates
• Dataflow: Qwik Start – Python
• Dataproc: Qwik Start – Console
• Dataproc: Qwik Start – Command Line
• Cloud Natural Language API: Qwik Start
• Speech-to-Text API: Qwik Start
• Video Intelligence: Qwik Start
• Prepare Data for ML APIs on Google Cloud: Challenge Lab

Module 3: Working with Notebooks in Vertex AI

• Mini-course: 8 lessons

Module 4: Create ML Models with BigQuery ML

• Getting Started with BigQuery ML
• Predict Visitor Purchases with a Classification Model
• Predict Taxi Fare with a Forecasting Model
• Bracketology with Google Machine Learning
• Create ML Models with BigQuery ML: Challenge Lab

Module 5: Engineer Data for Predictive Modeling with BigQuery ML

• Creating a Data Transformation Pipeline with Cloud Dataprep
• ETL Processing on Google Cloud Using Dataflow and BigQuery (Python)
• Predict Visitor Purchases with a Classification Model
• Engineer Data for Predictive Modeling: Challenge Lab

Module 6: Feature Engineering

• Introduction to Vertex AI Feature Store
• Raw Data to Features
• Feature Engineering
• Preprocessing and Feature Creation
• Feature Crosses: TensorFlow Playground
• Introduction to TensorFlow Transform

Module 7: TensorFlow on Google Cloud

• Introduction to the TensorFlow Ecosystem
• Design and Build an Input Data Pipeline
• Building Neural Networks with TensorFlow and Keras
• Training at Scale with Vertex AI

Module 8: Production Machine Learning Systems

• Architecting Production ML Systems
• Designing Adaptable ML Systems
• Designing High-Performance ML Systems
• Hybrid ML Systems
• Troubleshooting ML Production Systems

Module 9: Machine Learning Operations (MLOps): Getting Started

• Employing Machine Learning Operations
• Vertex AI and MLOps on Vertex AI

Module 10: Machine Learning Operations (MLOps) with Vertex AI: Manage Features

• Introduction to Vertex AI Feature Store
• An In-Depth Look

Module 11: Introduction to Generative AI

• Mini-course: 1 lesson

Module 12: Introduction to Large Language Models

• Mini-course: 1 lesson

Module 13: Machine Learning Operations (MLOps) for Generative AI

• Mini-course: 5 lessons

Module 14: Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation

• Introduction to Model Evaluation
• Model Evaluation for Generative AI

Module 15: ML Pipelines on Google Cloud

• Introduction to TFX Pipelines
• Pipeline Orchestration with TFX
• Custom Components and CI/CD for TFX Pipelines
• ML Metadata with TFX
• Continuous Training with Multiple SDKs, Kubeflow & AI Platform Pipelines
• Continuous Training with Cloud Composer
• ML Pipelines with MLflow

Module 16: Build and Deploy Machine Learning Solutions on Vertex AI

• Vertex AI: Qwik Start
• Identify Damaged Car Parts with Vertex AutoML Vision
• Deploy a BigQuery ML Customer Churn Classifier to Vertex AI
• Vertex Pipelines: Qwik Start
• Build and Deploy ML Solutions: Challenge Lab

Module 17: Build Generative AI Applications on Google Cloud

• Generative AI Applications
• Prompts
• Retrieval Augmented Generation (RAG)

Module 18: Responsible AI for Developers: Fairness and Bias

• AI Interpretability and Transparency
• Modernizing Infrastructure in the Cloud

Module 19: Responsible AI for Developers: Interpretability and Transparency

• AI Interpretability and Transparency
• Modernizing Infrastructure in the Cloud

Module 20: Responsible AI for Developers: Privacy and Safety

• AI Privacy
• AI Safety

Get Enrolled into this course

Why Choose This Certification?

Validates advanced skills in designing, building, and productionizing ML models using Google Cloud.
Official credential from Google Cloud—boosts your credibility in AI/ML hiring pipelines.
Recognized by top companies for deploying ML models at scale.
In-demand certification for roles involving MLOps and scalable ML systems.

Who Can Pursue This Certification ?

AI/ML professionals seeking specialization in Google Cloud.
Software engineers transitioning into machine learning.
Individuals aiming for roles in MLOps or production-grade AI.

Prerequisites

3+ years of industry experience, including 1+ year designing and managing ML solutions on Google Cloud.
Strong knowledge of Python, ML algorithms, TensorFlow or similar frameworks.
Familiarity with Google Cloud services like BigQuery, Vertex AI, Cloud Storage, etc.

Job Opportunities Available

Machine Learning Engineer (Google Cloud)
MLOps Engineer
AI Engineer (Cloud-based)
Data Scientist (Production ML)
AI/ML Consultant (GCP)

Key Benefits: What You Will Gain

Upon completing the Artificial Intelligence courses at Netsmartz School, learners will gain the following benefits:

Official Course Completion Badge/Certificate

Earn a prestigious course completion badge or certificate, available on the Netsmartz School and Microsoft, highlighting your proficiency in AI

Industry-Recognized Skills

Master key concepts and gain hands-on experience in Artificial Intelligence, making you well-equipped for real-world applications

Career Advancement

Enhance your career prospects by showcasing your expertise in AI to potential employers and staying ahead in today's rapidly evolving tech landscape

Why Netsmartz School

Experts-led Classroom Trainings

Our programs are crafted with insights from top industry minds and educators, ensuring you gain skills that truly matter in the real world.

Hands-On Experience on Real-World Projects

Take on projects that mirror the real challenges of the industry, work with real data, and build practical skills that lead to job-ready expertise.

Insights from Industry Leaders

Gain valuable insights from leading practitioners who bring industry use cases, case studies, and proven strategies into your classroom

Guidance and Support from Mentors

Stay on track with 24/7 access to mentors and a community of peers, all ready to help you overcome any challenges along the way.

AI Courses

CyberSecurity Courses

• AI Foundations on Google Cloud
• AI Development on Google Cloud
• ML Workflow and Vertex AI
• Generative AI on Google Cloud

• Mini-course: 8 lessons

• Getting Started with BigQuery ML
• Predict Visitor Purchases with a Classification Model
• Predict Taxi Fare with a Forecasting Model
• Bracketology with Google Machine Learning
• Create ML Models with BigQuery ML: Challenge Lab

• Creating a Data Transformation Pipeline with Cloud Dataprep
• ETL Processing on Google Cloud Using Dataflow and BigQuery (Python)
• Predict Visitor Purchases with a Classification Model
• Engineer Data for Predictive Modeling: Challenge Lab

• Introduction to Vertex AI Feature Store
• Raw Data to Features
• Feature Engineering
• Preprocessing and Feature Creation
• Feature Crosses: TensorFlow Playground
• Introduction to TensorFlow Transform

• Introduction to the TensorFlow Ecosystem
• Design and Build an Input Data Pipeline
• Building Neural Networks with TensorFlow and Keras
• Training at Scale with Vertex AI

• Architecting Production ML Systems
• Designing Adaptable ML Systems
• Designing High-Performance ML Systems
• Hybrid ML Systems
• Troubleshooting ML Production Systems

• Employing Machine Learning Operations
• Vertex AI and MLOps on Vertex AI

• Introduction to Vertex AI Feature Store
• An In-Depth Look

• Mini-course: 1 lesson

• Mini-course: 1 lesson

• Mini-course: 5 lessons

• Introduction to Model Evaluation
• Model Evaluation for Generative AI

• Introduction to TFX Pipelines
• Pipeline Orchestration with TFX
• Custom Components and CI/CD for TFX Pipelines
• ML Metadata with TFX
• Continuous Training with Multiple SDKs, Kubeflow & AI Platform Pipelines
• Continuous Training with Cloud Composer
• ML Pipelines with MLflow

• Vertex AI: Qwik Start
• Identify Damaged Car Parts with Vertex AutoML Vision
• Deploy a BigQuery ML Customer Churn Classifier to Vertex AI
• Vertex Pipelines: Qwik Start
• Build and Deploy ML Solutions: Challenge Lab

• Generative AI Applications
• Prompts
• Retrieval Augmented Generation (RAG)

• AI Interpretability and Transparency
• Modernizing Infrastructure in the Cloud

• AI Interpretability and Transparency
• Modernizing Infrastructure in the Cloud

• AI Privacy
• AI Safety

Get Enrolled into this course

Why Choose This Certification?

Who Can Pursue This Certification ?

Prerequisites

Job Opportunities Available

Official Course Completion Badge/Certificate

Industry-Recognized Skills

Career Advancement

Experts-led Classroom Trainings

Hands-On Experience on Real-World Projects

Insights from Industry Leaders

Guidance and Support from Mentors

AI Courses

CyberSecurity Courses

Google Professional Machine Learning Engineer

Master Machine Learning Skills for Real-World Impact