▶Book Description
AI Blueprints gives you a working framework and the techniques to build your own successful AI business applications. You'll learn across six business scenarios how AI can solve critical challenges with state-of-the-art AI software libraries and a well thought out workflow. Along the way you'll discover the practical techniques to build AI business applications from first design to full coding and deployment.
The AI blueprints in this book solve key business scenarios. The first blueprint uses AI to find solutions for building plans for cloud computing that are on-time and under budget. The second blueprint involves an AI system that continuously monitors social media to gauge public feeling about a topic of interest - such as self-driving cars. You'll learn how to approach AI business problems and apply blueprints that can ensure success.
The next AI scenario shows you how to approach the problem of creating a recommendation engine and monitoring how those recommendations perform. The fourth blueprint shows you how to use deep learning to find your business logo in social media photos and assess how people interact with your products. Learn the practical techniques involved and how to apply these blueprints intelligently. The fifth blueprint is about how to best design a 'trending now' section on your website, much like the one we know from Twitter. The sixth blueprint shows how to create helpful chatbots so that an AI system can understand customers' questions and answer them with relevant responses.
This book continuously demonstrates a working framework and strategy for building AI business applications. Along the way, you'll also learn how to prepare for future advances in AI. You'll gain a workflow and a toolbox of patterns and techniques so that you can create your own smart code.
▶What You Will Learn
⦁ An essential toolbox of blueprints and advanced techniques for building AI business applications
⦁ How to design and deploy AI applications that meet today's business needs
⦁ A workflow from first design stages to practical code solutions in your next AI projects
⦁ Solutions for AI projects that involve social media analytics and recommendation engines
⦁ Practical projects and techniques for sentiment analysis and helpful chatbots
⦁ A blueprint for AI projects that recommend products based on customer purchasing habits
⦁ How to prepare yourself for the next decade of AI and machine learning advancements
▶Key Features
⦁ Learn and master the essential blueprints to program AI for real-world business applications
⦁ Gain insights into how modern AI and machine learning solve core business challenges
⦁ Acquire practical techniques and a workflow that can build AI applications using state-of-the-art software libraries
⦁ Work with a practical, code-based strategy for creating successful AI solutions in your business
▶Who This Book Is For
This book is targeted at software engineers who are familiar with Java and Python and wish to learn how to use artificial intelligence and machine learning in their code. This book is not just a list of techniques and algorithms, however.
Every example project includes a detailed discussion of integration and deployment strategies and techniques for continuous evaluation of the AI after it is deployed.
The projects and suggested workflow target small/medium businesses and startup companies that wish to introduce advanced capabilities and automation into an existing platform.
▶What this book covers
⦁ Chapter 1, The AI Workflow, introduces the AI workflow, which is a series of four steps in a mature process for building and deploying AI. This chapter also discusses the role of AI in context of a larger software system. The chapter ends with an introduction to each of the projects developed in Chapter 2, A Blueprint for Planning Cloud Infrastructure to Chapter 7, A Blueprint for Understanding Queries and Generating Responses.
⦁ Chapter 2, A Blueprint for Planning Cloud Infrastructure, shows how to use the open source OptaPlanner constraint solver planning engine to create a plan for cloud computing resources. Given a time and money budget and a set of computing tasks to complete, this chapter develops a Java-based solution for the optimal number of cloud resources to complete the tasks in the shortest time and lowest budget. Benchmarks are detailed to show that the solution is accurate. Each step of the AI workflow is addressed to help readers prepare to deploy the solution.
⦁ Chapter 3, A Blueprint for Making Sense of Feedback, shows how to acquire feedback from customers and the general public about a company's products and services, and how to identify the sentiment, or general mood, of the feedback for particular products, services, or categories. The Twitter and Reddit APIs are demonstrated for acquiring feedback. Two approaches are demonstrated for sentiment analysis: a dictionary-based approach and a method using machine learning with the CoreNLP library. The sentiment data is then visualized with plotly.js in a dashboard view for real-time updates. Each step of the AI workflow is addressed to help readers prepare to deploy the solution.
⦁ Chapter 4, A Blueprint for Recommending Products and Services, shows how to build and deploy a recommendation engine for products and services. Given a history of all user activity (purchases, clicks, ratings), a system is designed that can produce appropriate recommendations for individual users. An overview of the relevant mathematics is included, and the Python implicit library is used for building the solution. A continuous evaluation methodology is detailed to ensure the recommender continues to provide appropriate recommendations after it is deployed. Each step of the AI workflow is addressed to help readers prepare to deploy the solution.
⦁ Chapter 5, A Blueprint for Detecting Your Logo in Social Media, shows how to build a convolutional neural network (CNN) to detect logos in other people's photos. Using the Python library TensorFlow, readers are shown how to take an existing pre-trained object recognition model such as Xception and refine it for detecting specific objects using a small training set of images. We also demonstrate the use of YOLO and compare results. Then the Twitter API code from Chapter 3, A Blueprint for Making Sense of Feedback is reused to acquire images from social media, and the detector is run on these images to pick out photos of interest. A short introduction to CNNs and deep learning is included. Each step of the AI workflow is addressed to help readers prepare to deploy the solution.
⦁ Chapter 6, A Blueprint for Discovering Trends and Recognizing Anomalies, explains how to discover and track trends on a blog, storefront, or social media platform, as well as recognizing anomalous events that defy the trends. Using statistical models and anomaly detection algorithms, code is developed with the Python library scikitlearn. Different approaches are compared to address different use cases. Each step of the AI workflow is addressed to help readers prepare to deploy the solution.
⦁ Chapter 7, A Blueprint for Understanding Queries and Generating Responses, shows how to build and deploy an automated helpdesk chatbot. Using the Rasa Python library and Prolog coding, two custom chatbots are developed that examine the user's question and construct an appropriate answer using natural language generation. The Prolog code helps us develop a logical reasoning agent that is able to answer complex questions. Each step of the AI workflow is addressed to help readers prepare to deploy the solution.
⦁ Chapter 8, Preparing for Your Future and Surviving the Hype Cycle, examines the current state and near future of artificial intelligence. It examines the hype cycle and the dramatic shifts in popular interest in AI over the years and provides guidance for how to be successful in such unpredictable environments. The chapter includes advice for how to identify new approaches and advances in AI and how to decide whether or not these advances are relevant for real business needs.
