Google Cloud Machine Learning Engineer Certification Training
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Google Cloud Machine Learning Engineer Certification Training

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Gain expertise in machine learning on Google Cloud with this certification training. Learn to build, deploy, and optimize ML models and prepare for the Google Cloud Machine Learning Engineer Certification exam.

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Overview

The Google Cloud Machine Learning Engineer Certification Training is designed to provide you with the skills and knowledge to build scalable and reliable machine learning models on Google Cloud. This course covers data preparation, model training, deployment, monitoring, and optimization using Google Cloud tools like Vertex AI. Through hands-on labs, real-world projects, and expert-led instruction, you’ll gain practical experience and the confidence to ace the certification exam. Propel your career as a certified Machine Learning Engineer in the rapidly growing AI and cloud computing fields.

Key Features

  • Certification Focus
  • In-Depth Curriculum
  • Industry-Expert Instructors
  • Real-World Projects

Curriculum

Study Plan

1Module 1: Google Cloud Big Data and Machine Learning Fundamentals
  • Big Data and Machine Learning on Google Cloud
  • Data Engineering for Streaming Data
  • Big Data with BigQuery
  • Machine Learning Options on Google Cloud
  • The Machine Learning Workflow with Vertex AI Hands-on      
2Module 2: How Google Does Machine Learning
  • What It Means to be AI-First
  • How Google Does ML
  • Machine Learning Development with Vertex AI
  • Machine Learning Development with Vertex Notebooks
  • Best Practices for Implementing Machine Learning on Vertex AI
  • Responsible AI Development
3Module 3: Launching into Machine Learning
  • Introduction
  • Get to Know Your Data: Improve Data through Exploratory Data Analysis
  • Machine Learning in Practice
  • Training AutoML Models Using Vertex AI
  • BigQuery Machine Learning: Develop ML Models Where Your Data Lives
  • Optimization
  • Generalization and Sampling
4Module 4: TensorFlow on Google Cloud
  • Introduction to the TensorFlow ecosystem
  • Design and Build an Input Data Pipeline
  • Building Neural Networks with the TensorFlow and Keras API
  • Training at Scale with Vertex AI
5Module 5: 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
6Module 6: Machine Learning in the Enterprise
  • Introduction
  • Understanding the ML Enterprise Workflow
  • Data in the Enterprise
  • Science of Machine Learning and Custom Training
  • Vertex Vizier Hyperparameter Tuning
  • Prediction and Model Monitoring UsingVertex AI
  • Vertex AI Pipelines
  • Best Practices for ML Development
7Module 7: Machine Learning in the Enterprise
  • Machine Learning (ML) on Google Cloud Platform (GCP)
  • Explore the Data
  • Create the dataset
  • Build the Model
  • Operationalize the model
8Module 8: Production Machine Learning Systems
  • Introduction to Advanced Machine Learning on Google Cloud
  • Architecting Production ML Systems 
  • Designing Adaptable ML Systems
  • Designing High-Performance ML Systems
  • Building Hybrid ML Systems
9Module 9: Computer Vision Fundamentals with Google Cloud
  • Introduction to Computer Vision and Pre-built ML Models for Image Classification
  • Vertex AI and AutoML Vision on Vertex AI
  • Custom Training with Linear, Neural Network and Deep Neural Network models
  • Convolutional Neural Networks
  • Dealing with Image Data
10Module 10: Sequence Models for Time Series and Natural Language Processing on Google Cloud
  • Working with Sequences 
  • Recurrent Neural Networks
  • Dealing with Longer Sequences
  • Text Classification
  • Reusable Embeddings
  • Encoder-Decoder Models
11Module 11: Recommendation Systems on Google Cloud
  • Recommendation Systems Overview
  • Content-Based Recommendation Systems
  • Collaborative Filtering Recommendations Systems
  • Neural Networks for Recommendation Systems
  • Reinforcement Learning
12Module 12: MLOps (Machine Learning Operations) Fundamentals
  • Why and When do we Need MLOps
  • Understanding the Main Kubernetes Components (Optional)
  • Introduction to AI Platform Pipelines
  • Training, Tuning and Serving on AI Platform
  • Kubeflow Pipelines on AI Platform
  • CI/CD for Kubeflow Pipelines on AI Platform
13Module 13: 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

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