| Description |
1 online resource (xxv, 373 pages) : illustrations (chiefly color) |
| 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. |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Subject |
Artificial intelligence -- Industrial applications.
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Cloud computing.
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Python (Computer program language)
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Intelligence artificielle -- Applications industrielles. |
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Infonuagique. |
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Python (Langage de programmation) |
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Artificial intelligence -- Industrial applications |
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Cloud computing |
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Python (Computer program language) |
| Other Form: |
Print version: Walsh, Barry Productionizing AI Berkeley, CA : Apress L. P.,c2023 9781484288160 |
| ISBN |
9781484288177 (electronic bk.) |
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1484288173 (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-8817-7 doi |
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