Google Cloud Machine Learning Engineer Certification Training

• 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

Hands-On

• Vertex AI: Qwik Start
• Using an Image Dataset to Train an AutoML Model

• 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

Hands-On

• Exploratory Data Analysis Using Python and BigQuery
• Using BigQuery ML to Predict Penguin Weight

• 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

Hands-On

• Classifying Structured Data using Keras Preprocessing Layers
• Build a DNN using the Keras Functional API

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

Hands-On

• Using Feature Store
• Performing Basic Feature Engineering in BQML
• Performing Basic Feature Engineering in Keras

• 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 Using Vertex AI
• Vertex AI Pipelines
• Best Practices for ML Development

Hands-On

• Vertex Pipelines: Qwik Start
• Cloud Natural Language API: Qwik Start
• Google Cloud Speech API: Qwik Start
• Video Intelligence: Qwik Start

• Machine Learning (ML) on Google Cloud Platform (GCP)
• Explore the Data
• Create the dataset
• Build the Model
• Operationalize the model

Hands-On

• Identify Damaged Car Parts with Vertex AutoML Vision
• Deploy a BigQuery ML Customer Churn Classifier to Vertex AI for Online Predictions

• 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

Hands-On

• Structured data prediction using Vertex AI Platform
• Serving ML Predictions in Batch and Real Time
• Distributed Training with Keras
• Using Kubeflow Pipelines with AI Platform

• 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

Hands-On

• Using the What-If Tool with Image Recognition Models
• Identifying Bias in Mortgage Data using Cloud AI Platform and the What-if Tool
• Compare Cloud AI Platform Models using the What-If Tool to Identify potential bias

• Working with Sequences
• Recurrent Neural Networks
• Dealing with Longer Sequences
• Text Classification
• Reusable Embeddings
• Encoder-Decoder Models

Hands-On

• Time Series Prediction with a DNN Model
• Time Series Prediction with a Two-Layer RNN Model
• Text Classification using TensorFlow/Keras on AI Platform
• Text generation using tensor2tensor on Cloud AI Platform

• Recommendation Systems Overview
• Content-Based Recommendation Systems
• Collaborative Filtering Recommendations Systems
• Neural Networks for Recommendation Systems
• Reinforcement Learning

Hands-On

• Using Neural Networks for Content-Based Filtering
• Collaborative Filtering on Google Analytics data
• ML on GCP: Hybrid Recommendations with the MovieLens Dataset
• Applying Contextual Bandits for Recommendations with Tensorflow and TF-Agents

• 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

Hands-On

• Working with Cloud Build
• Creating Google Kubernetes Engine Deployments
• Using custom containers with AI Platform Training
• Continuous Training Pipeline with Kubeflow Pipeline and Cloud AI Platform
• CI/CD for a Kubeflow pipeline

• 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

Hands-On

• TFX Standard Components Walkthrough
• TFX on Cloud AI Platform Pipelines
• CI/CD for a TFX pipeline
• Continuous Training Pipelines with Cloud Composer