Hands-On Artificial Intelligence on Google Cloud Platform by Anand Deshpande -- Read -- Imperial Library of Trantor

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Hands-On Artificial Intelligence on Google Cloud Platform

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About the authors About the reviewers Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Section 1: Basics of Google Cloud Platform Overview of AI and GCP

Understanding the Cloud First strategy for advanced data analytics

Advantages of a Cloud First strategy Anti-patterns of the Cloud First strategy

Google data centers Overview of GCP AI building blocks

Data

Storage Processing Actions

Natural language processing Speech recognition Machine vision Information processing and reasoning Planning and exploring Handling and control Navigation and movement Speech generation Image generation

AI tools available on GCP

Sight Language Conversation

Summary

Computing and Processing Using GCP Components

Understanding the compute options

Compute Engine

Compute Engine and AI applications

App Engine

App Engine and AI applications

Cloud Functions

Cloud Functions and AI applications

Kubernetes Engine

Kubernetes Engine and AI applications

Diving into the storage options

Cloud Storage

Cloud Storage and AI applications

Cloud Bigtable

Cloud Bigtable and AI applications

Cloud Datastore

Cloud Datastore and AI applications

Cloud Firestore

Cloud Firestore and AI applications

Cloud SQL

Cloud SQL and AI applications

Cloud Spanner

Cloud Spanner and AI applications

Cloud Memorystore

Cloud Memorystore and AI applications

Cloud Filestore

Cloud Filestore and AI applications

Understanding the processing options

BigQuery

BigQuery and AI applications

Cloud Dataproc

Cloud Dataproc and AI applications

Cloud Dataflow

Cloud Dataflow and AI applications

Building an ML pipeline

Understanding the flow design Loading data into Cloud Storage Loading data to BigQuery Training the model Evaluating the model Testing the model

Summary

Section 2: Artificial Intelligence with Google Cloud Platform Machine Learning Applications with XGBoost

Overview of the XGBoost library

Ensemble learning

How does ensemble learning decide on the optimal predictive model?

Reducible errors – bias Reducible errors – variance Irreducible errors Total error

Gradient boosting eXtreme Gradient Boosting (XGBoost)

Training and storing XGBoost machine learning models Using XGBoost trained models Building a recommendation system using the XGBoost library

Creating and testing the XGBoost recommendation system model

Summary

Using Cloud AutoML

Overview of Cloud AutoML

The workings of AutoML AutoML API overview

REST source – pointing to model locations REST source – for evaluating the model REST source – the operations API

Document classification using AutoML Natural Language

The traditional machine learning approach for document classification Document classification with AutoML

Navigating to the AutoML Natural Language interface Creating the dataset Labeling the training data Training the model Evaluating the model

The command line Python Java Node.js

Using the model for predictions

The web interface A REST API for model predictions Python code for model predictions

Image classification using AutoML Vision APIs

Image classification steps with AutoML Vision

Collecting training images

Creating a dataset

Labeling and uploading training images Training the model Evaluating the model

The command-line interface Python code

Testing the model

Python code

Performing speech-to-text conversion using the Speech-to-Text API

Synchronous requests Asynchronous requests Streaming requests

Sentiment analysis using AutoML Natural Language APIs Summary

Building a Big Data Cloud Machine Learning Engine

Understanding ML Understanding how to use Cloud Machine Learning Engine

Google Cloud AI Platform Notebooks

Google AI Platform deep learning images Creating Google Platform AI Notebooks Using Google Platform AI Notebooks Automating AI Notebooks execution

Overview of the Keras framework Training your model using the Keras framework Training your model using Google AI Platform Asynchronous batch prediction using Cloud Machine Learning Engine Real-time prediction using Cloud Machine Learning Engine Summary

Smart Conversational Applications Using DialogFlow

Introduction to DialogFlow

Understanding the building blocks of DialogFlow

Building a DialogFlow agent

Use cases supported by DialogFlow

Performing audio sentiment analysis using DialogFlow Summary

Section 3: TensorFlow on Google Cloud Platform Understanding Cloud TPUs

Introducing Cloud TPUs and their organization

Advantages of using TPUs

Mapping of software and hardware architecture

Available TPU versions Performance benefits of TPU v3 over TPU v2 Available TPU configurations Software architecture

Best practices of model development using TPUs

Guiding principles for model development on a TPU

Training your model using TPUEstimator

Standard TensorFlow Estimator API TPUEstimator programming model TPUEstimator concepts Converting from TensorFlow Estimator to TPUEstimator

Setting up TensorBoard for analyzing TPU performance Performance guide

XLA compiler performance Consequences of tiling Fusion

Understanding preemptible TPUs

Steps for creating a preemptible TPU from the console Preemptible TPU pricing Preemptible TPU detection

Summary

Implementing TensorFlow Models Using Cloud ML Engine

Understanding the components of Cloud ML Engine

Training service

Using the built-in algorithms Using a custom training application

Prediction service Notebooks Data Labeling Service Deep learning containers

Steps involved in training and utilizing a TensorFlow model

Prerequisites Creating a TensorFlow application and running it locally

Project structure recommendation Training data

Packaging and deploying your training application in Cloud ML Engine Choosing the right compute options for your training job

Choosing the hyperparameters for the training job

Monitoring your TensorFlow training model jobs Summary

Building Prediction Applications

Overview of machine-based intelligent predictions

Understanding the prediction process

Maintaining models and their versions Taking a deep dive into saved models

SignatureDef in the TensorFlow SavedModel TensorFlow SavedModel APIs

Deploying the models on GCP

Uploading saved models to a Google Cloud Storage bucket Testing machine learning models Deploying models and their version

Model training example Performing prediction with service endpoints Summary

Section 4: Building Applications and Upcoming Features Building an AI application

A step-by-step approach to developing AI applications

Problem classification

Classification Regression Clustering Optimization Anomaly detection Ranking Data preparation

Data acquisition Data processing Problem modeling Validation and execution

Holdout Cross-validation Model evaluation parameters (metrics) Classification metrics

Model deployment

Overview of the use case – automated invoice processing (AIP) Designing AIP with AI platform tools on GCP

Performing optical character recognition using the Vision API Storing the invoice with Cloud SQL

Creating a Cloud SQL instance Setting up the database and tables Enabling the Cloud SQL API Enabling the Cloud Functions API Creating a Cloud Function Providing the Cloud SQL Admin role

Validating the invoice with Cloud Functions Scheduling the invoice for the payment queue (pub/sub) Notifying the vendor and AP team about the payment completion Creating conversational interface for AIP

Upcoming features Summary

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