Productionizing AI: How to Deliver AI B2B Solutions with Cloud and Python

Description

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 monthDeploy AI solutions at a low costWork around Big Tech dominance and develop MVPs on the cheapCreate 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. 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 Introduction to Data Ingestion Data Ingestion – The Challenge Today The AI Ladder Cloud Architectures/Cloud “Stack” Scheduled (OLAP) vs. Streaming (OLTP) Data APIs Data Types (Structured vs. Unstructured) File Types Automated Data Ingestion: Hands-on Practice Working with Parquet: Hands-on Practise Data Stores for AI Data Stores: Data Lakes and Data Warehouses Lakehouses Scoping Project Data Requirements OLTP/OLAP – Determining the Best Approach ETL vs. ELT SQL vs. NoSQL Databases Elasticity vs. Scalability Data Stores for AI: Hands-on Practice Cloud Services for Data Ingestion Cloud (SQL) Data Warehouses Data Lake Storage Hadoop Stream Processing and Stream Analytics Simple Data Streaming: Hands-on Practise Cloud services for Data Ingestion: Hands-on Practise Data Pipeline Orchestration – Best Practice Storage Considerations Data Ingestion Schedules Serverless Computing End-of-day Processes Data Import for Machine and Deep Learning Building a Delivery Pipeline Example: XenonStack Example: Red Hat/IBM Example: AWS Serverless Architecture Example: Databricks with Apache Spark Example: Snowflake Workload Management Data Pipeline Orchestration: Hands-on Practice Wrap-up Chapter 4: Machine Learning on Cloud ML Fundamentals Supervised Machine Learning Classification and Regression Time Series Forecasting Introduction to fbprophet: Hands-on Practice Unsupervised Machine Learning Clustering Dimensionality Reduction Unsupervised Machine Learning (Clustering): Hands-on Practice Semisupervised Machine Learning Machine Learning Implementation Exploratory Data Analysis (EDA) Data Wrangling Feature Engineering Shuffling and Data Partitioning/Splitting Sampling End-to-End Wrangling: Hands-on Practice Algorithmic Modelling Performance Benchmarking Continual Improvement Machine Learning Classifiers: Hands-on Practice Model Selection, Deployment, and Inference Inference: Hands-on Practice Reinforcement Learning Wrap-up Chapter 5: Neural Networks and Deep Learning Introduction to Deep Learning What Is Deep Learning Deep Learning – Why Now? AI and Deep Learning Hype Cycle High-Level Architectures TensorFlow Playground: Hands-on Practice Stochastic Processes Generative vs. Discriminative Random Walks Markov Chains and Markov Processes Other Stochastic Processes: Martingales Implementing a Random Walk in Python: Hands-on Practice Introduction to Neural Networks Artificial Neural Networks (ANNs) The Simple Perceptron Multilayer Perceptron (MLP) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) networks Other Types of Neural Networks Restricted Boltzmann Machines (RBMs) Deep Belief Networks (DBNs) Deep Boltzmann Machines (DBMs) Autoencoders Generative Adversarial Networks A Simple Deep Learning Solution – MNIST: Hands-on Practice Autoencoders in Keras: Hands-on Practice Deep Learning Tools Tools for Deep Learning TensorFlow Keras PyTorch Other Important Deep Learning Tools Apache Spark Frameworks for Deep Learning and Implementation Tensors Key TensorFlow Concepts The Deep Learning Modeling Lifecycle Sequential and Functional Model APIs Sequential Model API Functional Model API Implementing a CNN Implementing an RNN LSTM Implementation for Time Series Neural Networks – Terminology Computing the Output of a Multilayer Neural Network Convolutional Neural Networks with Keras and TensorFlow: Hands-on Practice Recurrent Neural Networks – Time Series Forecasting: Hands-on Practice Tuning a DL Model Activation Functions (Logistic) sigmoid function Hyperbolic Tangent Function (tanh) Rectified Linear Unit (ReLU) Softmax Gradient Descent and Backpropagation Backpropagation Other Optimization Algorithms SGD with Momentum AdaGrad, Adadelta, and RMSProp Adam Loss Functions Improving DL Performance Deep Learning Best Practice – Hyperparameters Network Tuning Deeper Network/More Layers/More Neurons Activation Function Neural Network Ensembles Batch Normalization Pooling Image Augmentation Process Tuning Number of Epochs and Batch Size Learning Rate Regularization Dropout Early Stopping Transfer Learning Wrap-up Softmax: Hands-on Practice Early Stopping: Hands-on Practice Chapter 6: AutoML, AutoAI, and the Rise of NoLo UIs Machine Learning: Process Recap Global Search Algorithms Bayesian Optimization and Inference Bayesian Inference: Hands-on Practice Python-Based Libraries for Automation PyCaret auto-sklearn Auto-WEKA TPOT Python Automation with TPOT: Hands-on Practice AutoAI Tools and Platforms IBM Cloud Pak for Data Azure Machine Learning Google Cloud Vertex AI Google Cloud Composer AWS SageMaker Autopilot TensorFlow Extended (TFX) Wrap-up AutoAI with IBM Cloud Pak for Data: Hands-on Practice Healthcare diagnostics with Google Teachable Machines: Hands-on Practice TFX and Vertex AI Pipelines: Hands-on Practice Azure Video Analyzer: Hands-on Practice Chapter 7: AI Full Stack: Application Development Introduction to AI Application Development Developing an AI Solution AI Apps – Up and Running APIs and Endpoints Distributed Processing and Clusters Clusters Graphical Processing Units (GPUs) TensorFlow Processing Units (TPUs) Sharding Virtual Environments Running Python from Terminal: Hands-on Practice API Web Services and Endpoints: Hands-on Practice AI Accelerators - GPUs: Hands-on Practise Software and Tools for AI Development AI Needs Data and Cloud Cloud Platforms AWS Azure GCP IBM Cloud Heroku Python-Based UIs Flask Dash Django Other AI Software Vendors ONNX (Open Neural Network Exchange) C3 DataRobot Introduction to Dash: Hands-on Practice Flask: Hands-on Practice Introduction to Django: Hands-on Practice ML Apps Developing Machine Learning Applications Customer Experience Fraud Detection and Cybersecurity Operations Management, Decision, and Business Support Risk Management, and Portfolio and Asset Optimization Developing a Recommendation Engine: Hands-on Practice Portfolio Optimization Accelerator: Hands-on Practise DL Apps Developing Deep Learning Applications Key Deep Learning Apps Computer Vision Forecasting IoT Full-Stack Deep Learning: Hands-on Practice Wrap-up Chapter 8: AI Case Studies Industry Case Studies Business/Organizational Demand for AI AI Enablers AI Solutions by Vertical Industry AI Use Cases – Solution Frameworks Solution Architectures Example: Azure Telco Solutions Specific Challenges Solution Categories Real-time Dashboards Sentiment Analysis Predictive Analytics Connecting to the Twitter API from Python Twitter API and Basic Sentiment Analysis: Hands-on Practice Retail Solutions Challenges in the Retail Industry Churn and Retention Modelling A Best Practice Approach to Modelling Churn Model Design and Outcomes Online Retail Predictive Analytics with GCP BigQuery: Hands-on Practice Predicting Customer Churn: Hands-on Practise Social Network Analysis: Hands-on Practise Banking and Financial Services/FinTech Solutions Industry Challenges Fraud Detection Case Study: AWS Fraud Detection AWS Fraud Detection with AWS SageMaker: Hands-on Practice Supply Chain Solutions Challenges Across Supply Chains Predictive Analytics Solutions Supply Chain Optimization and Prescriptive Analytics Supply Chain Optimization with IBM CloudPak/Watson Studio: Hands-on Practice Oil and Gas/Energy and Utilities Solutions Challenges in Energy, Oil, and Gas Sectors AI Solutions in Energy – An Opportunity or a Threat? Healthcare and Pharma Solutions Healthcare – The AI Gap Healthcare and Pharma Solutions HR Solutions HR in 2002 Sample HR Solutions HR Employee Attrition: Hands-on Practice Other Case Studies Public Sector and Government Manufacturing Cybersecurity Insurance/Telematics Legal DALL-E for the Creative Arts: Hands-on Practise Wrap-up Chapter 9: Deploying an AI Solution (Productionizing and Containerization) Productionizing an AI Application Typical Barriers to Production Cloud/CSP Roulette Simplifying the AI Challenge – Start Small, Stay Niche Database Management in Python: Hands-on Practice App Building on GCP: Hands-on Practice PowerBI – Python Handshake: Hands-on Practice AI Project Lifecycle Design Thinking Through to Agile Development Driving Development Through Hypothesis Collaborate, Test, Measure, Repeat Continual Process Improvement Data drift Automated Retraining Hosting on Heroku – End-to-End: Hands-on Practice Enabling Engineering and Infrastructure The AI Ecosystem – The AI Cloud Stack Data Lake Deployment – Best Practice Data Pipeline Operationalization and Orchestration Big Data Engines and Parallelization Dask Leveraging S3 File Storage: Hands-on Practise Apache Spark Quick Start on Databricks: Hands-on Practice Dask Parallelization: Hands-on Practice Full Stack and Containerization…the final frontier Full Stack AI – React and Flask Case Study Deploying on Cloud with a Docker Container Implementing a Continuous Delivery Pipeline Wrap-up DL App deployment with Streamlit and Heroku: Hands-on Practice Deploying on Azure with a Docker Container: Hands-on Practice Chapter 10: Natural Language Processing Introduction to NLP NLP Fundamentals Historical Context and Development of NLP NLP Goals and Sector-specific Use Cases Key Industrial Applications The NLP Lifecycle From Parsing to Linguistic Analysis Word Embeddings to Deep Learning Creating a Word Cloud: Hands-on Practice Preprocessing and Linguistics Preprocessing/Initial Cleaning Regular Expressions Text Stripping (e.g., HTML tags) Linguistics and Data Transformation Lexical Analysis Removing Stop Words Tokenization Syntactic Analysis Switch to Lowercase Part of Speech (POS) Tagging Named Entity Recognition (NER) Handling Contractions Stemming Semantic Analysis Lemmatization Disambiguation N-grams Text Parsing with NLTK: Hands-on Practice Text Vectorization, Word Embeddings, and Modelling in NLP Rule-Based/Frequency-Based Embedding One Hot Encoding and Count Vectorization Bag of Words (BoW) Latent Semantic Analysis (LSA) TF-IDF A Word on Cosine Similarity Word Embeddings/Prediction-Based Embedding Word2Vec (Google) Other Models NLP Modeling Text Summarization Topic Modeling Sequence Models Transformers and Attention Models Word Embeddings: Hands-on Practice Seq2Seq: Hands-on Practice PyTorch NLP: Hands-on Practice Tools and Applications of NLP Python Libraries NLP Applications Text Analytics Text-to-Speech-to-Text Social Media Sentiment Analysis/Opinion Mining Chatbots, Conversational Assistants, and IVAs NLP 2.0 Natural Language Generation Debating Auto-NLP Wrap-up WATSON Assistant Chatbot/IVA: Hands-on Practice Transformers for Chatbots: Hands-on Practice Postscript Wrap-up Epilogue Index