Cover image for 20200406PAIML-Raw-Unedited

20200406PAIML-Raw-Unedited

Release Date: 2020/12/01
ISBN: 20200406PAIML
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This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. Many resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure) using Python. This is a project-based book with extensive hands-on assignments. Based on material taught at leading universities.

Table of Contents

  1. Chapter One: Getting Started
    1. Effective Async Technical Discussions
    2. Effective Async Technical Project Management
    3. Cloud Onboarding for AWS, GCP, and Azure
    4. Steps to run this project
  2. Chapter 2: Cloud Computing Foundations
    1. Why you should consider using a cloud based development environment
    2. Overview of Cloud Computing
    3. PaaS Continuous Delivery
    4. IaC (Infrastructure as Code) w/ Terraform
    5. What is Continuous Delivery and Continuous Deployment?
    6. Continuous Delivery for Hugo Static Site from Zero
  3. Chapter3: Virtualization & Containerization
    1. CPU, Memory, I/O
    2. Elastic Resources
    3. Containers: Docker
    4. Container Registries
    5. Kubernetes in the Cloud
    6. Hybrid and Multi-cloud Kubernetes
    7. Running Kubernetes locally with Docker Desktop and sklearn flask
    8. Operationalizing a Microservice Overview
    9. Creating a Locust Loadtest with Flask
    10. Serverless Best Practices, Disaster Recovery and Backups for Microservices
  4. Chapter 4: Challenges and Opportunities in Distributed Computing
    1. Eventual Consistency
    2. CAP Theorem
    3. Amdahl’s Law
    4. Elasticity
    5. Highly Available
    6. End of Moore’s Law
    7. ASICS: GPUs, TPUs, FPGA
  5. Chapter 5: Cloud Storage
    1. Data Governance
    2. Cloud Databases
    3. Key Value Databases
    4. Graph Databases
    5. Cloud Object Storage: Amazon S3, GCP Cloud Storage, Amazon Glacier, Data Lakes, OpenStack Swift
    6. Amazon S3
    7. Batch vs Streaming Data and Machine Learning
    8. Cloud Data Warehouse
    9. GCP Big Query
    10. AWS Redshift
    11. Distributed File Systems: Red Hat Ceph, Amazon EFS (Elastic File System), HDFS
  6. Chapter 6: Serverless
    1. AWS Lambda
    2. Developing AWS Lambda Functions with AWS Cloud9
    3. AWS Step Functions
    4. Building a serverless data engineering pipeline
    5. Faas (Function as a Service)
    6. Chalice Framework on AWS Lambda
    7. Serverless
    8. Google Cloud Functions
    9. Kubernetes FaaS with GKE
    10. Azure Functions
    11. AWS Cloud-Native Primitives Overview
    12. AWS SQS
    13. AWS SNS
    14. AWS Cognito
    15. AWS API Gateway
    16. Google Cloud Shell Development Environment
    17. Google App Engine
  7. Chapter7: Big Data Platforms
    1. Cloud Object Storage
    2. Amazon S3
    3. Batch vs Streaming Data and Machine Learning
    4. Batch Processing: EMR/Hadoop, AWS Batch
    5. Cloud ETL
    6. Real-World Problems with ETL Building a Social Network From Scratch
    7. Stream Processing: EMR/Spark, AWS Kinesis, Kafka
  8. Chapter 8: Managed Machine Learning Systems, Platforms and AutoML
    1. Jupyter Notebook Workflow
    2. AutoML Overview
    3. AWS Sagemaker Overview
    4. AWS Sagemaker Elastic Architecture
    5. AWS Sagemaker Autopilot
    6. GCP AI Platform
    7. GCP AutoML Overview
    8. GCP AutoML Vision
    9. GCP AutoML Tables
    10. Azure ML Studio
    11. H20 AutoML
    12. Open Source ML Platforms Overview
    13. Ludwig
  9. Chapter9: Edge Computing
    1. IoT Overview
    2. AWS Greengrass
    3. Raspberry Pi
    4. Edge Machine Learning Solutions Overview
    5. Google AutoML
    6. Tensorflow lite
    7. Intel Movidius
    8. Apple X12
  10. Chapter 10: Data Science Case Studies and Projects
    1. Case Study: Datascience meets intermittent fasting
    2. Case Study: Coronavirus Epidemic
    3. Applied Computer Vision Overview
    4. Project: AWS DeepLense Edge Computer Vision
    5. Project: Rasberry Pi
    6. Project: Intel Movidius Edge Computer Vision
    7. Project: Serverless Data Engineering Pipelines
    8. Project: Operationalizing Containerized Machine Learning Models
    9. Project: Continuous Delivery of GCP PaaS
    10. Project: Using Docker Containers and Registeries
    11. Project: Cloud Machine Learning with Kubernetes
  11. Chapter 11: Essays
    1. Why There Will Be No Data Science Job Titles By 2029
    2. Exploiting The Unbundling Of Education
    3. How Vertically Integrated AI Stacks Will Affect IT Organizations
    4. Here Come The Notebooks
    5. Cloud Native Machine Learning And AI
    6. One Million Trained by 2021
    7. GI versus NoGi Brazilian Jiu-Jitsu
    8. Do They Know What Good Is?
  12. Chapter 12: Cloud Certifications
    1. AWS Certification Guide Overview
    2. AWS Certified Cloud Practitioner
    3. AWS Certified Solutions Architect
    4. AWS Certified Developer
    5. AWS Certified Data Analytics Specialty
    6. AWS Certified Machine Learning Specialty
    7. GCP Certification Guide Overview
    8. Azure Certification Guide Overview
  13. Chapter 13: Career
    1. Getting a job by becoming a Triple Threat
    2. How to build a Portfolio
    3. How to learn
    4. Create your own 20% Time
    5. Pear Revenue Strategy
    6. Remote First (Mastering Async Work)
    7. Getting a Job: Don’t Storm the Castle, Walk in the backdoor
    8. Motivation: Four WHATS
  14. Key Terms and Industry Jargon
    1. Build Server