Productionizing AI : How to Deliver AI B2B Solutions with Cloud and Python / Barry Walsh.
Walsh, Barry, author.
2023
TA347.A78
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Title Productionizing AI : How to Deliver AI B2B Solutions with Cloud and Python / Barry Walsh.
Author Walsh, Barry, author.
ISBN 9781484288177 (electronic bk.)
1484288173 (electronic bk.)
9781484288160
1484288165
DOI https://doi.org/10.1007/978-1-4842-8817-7
Published New York, NY : Apress L. P., [2023]
Language English
Description 1 online resource (xxv, 373 pages) : illustrations (chiefly color)
Item Number 10.1007/978-1-4842-8817-7 doi
Call Number TA347.A78
Dewey Decimal Classification 006.3
Summary This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app. From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions. The book is intended for those whose role demands overcoming budgetary barriers or constraints in accessing cloud credits to undertake the often difficult process of developing and deploying an AI solution. What You Will Learn Develop and deliver production-grade AI in one month Deploy AI solutions at a low cost Work around Big Tech dominance and develop MVPs on the cheap Create demo-ready solutions without overly complex python scripts/notebooks Who this book is for: Data scientists and AI consultants with programming skills in Python and driven to succeed in AI.
Access Note Access limited to authorized users.
Source of Description Description based upon print version of record.
Available in Other Form Productionizing AI
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Intro
Table of Contents
About the Author
About the Technical Reviewer
Preface
Prologue
Chapter 1: Introduction to AI and the AI Ecosystem
The AI Ecosystem
The Hype Cycle
Historical Context
AI - Some Definitions
AI Today
Machine Learning
Deep Learning
What Is Artificial Intelligence
Cloud Computing
CSPs - What Do They Offer ?
The Wider AI Ecosystem
Full-Stack AI
AI Ethics and Risk: Issues and Concerns
The AI ecosystem: Hands-on Practise
Applications of AI
Machine Learning
Deep Learning

Portfolio, Risk Management, and Forecasting
Natural Language Processing (NLP)
Chatbots
Cognitive Robotic Process Automation (CRPA)
Other AI Applications
AI Applications: Hands-on Practice
Data Ingestion and AI Pipelines
AI Engineering
What Is a Data Pipeline?
Extract, Transform, and Load (ETL)
Extract
Transform
Load
Data Wrangling
Performance Benchmarking
AI Pipeline Automation - AutoAI
Build Your Own AI Pipeline: Hands-on Practice
Neural Networks and Deep Learning
Machine Learning
Supervised Machine Learning

Unsupervised Machine Learning
Reinforcement Learning
What Is a Neural Network?
The Simple Perceptron
Deep Learning
Convolutional Neural Networks
Recurrent Neural Networks
Autoencoders and Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Neural Networks - terminology
Tools for Deep Learning
Introduction to Neural Networks and DL: Hands-on Practice
Productionizing AI
Compute and Storage
The CSPs - Why No-one Can Be Successful in AI Without Investing in Amazon, Microsoft, or Google
Compute Services
Storage Services

Containerization
Docker and Kubernetes
Productionizing AI: Hands-on Practice
Wrap-up
Chapter 2: AI Best Practice and DataOps
Introduction to DataOps and MLOps
DataOps
The Data "Factory"
The Problem with AI: From DataOps to MLOps
Enterprise AI
GCP/BigQuery: Hands-on Practice
Event Streaming with Kafka: Hands-on Practice
Agile
Agile Teams and Collaboration
Development/Product Sprints
Benefits of Agile
Adaptability
react.js: Hands-on Practice
VueJS: Hands-on Practise
Code Repositories
Git and GitHub
Version Control

Branching and Merging
Git Workflows
GitHub and Git: Hands-on Practice
Deploying an App to GitHub Pages: Hands-on Practice
Continuous Integration and Continuous Delivery (CI/CD)
CI/CD in DataOps
Introduction to Jenkins
Maven
Containerization
Docker and Kubernetes
Play With Docker: Hands-on Practice
Testing, Performance Evaluation, and Monitoring
Selenium
TestNG
Issue Management
Jira
ServiceNow
Monitoring and Alerts
Nagios
Jenkins CI/CD and Selenium Test Scripts: Hands-on Practice
Wrap-up
Chapter 3: Data Ingestion for AI

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