Cloud Computing for Data Analysis

Cloud Computing for Data Analysis

by Noah Gift, Alfredo Deza
Released December 2020
Publisher(s): Pragmatic AI Labs
ISBN: None

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Book description

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. Table of Contents
  2. Introduction
    1. About the Cover
    2. What you will learn
  3. Chapter One: Getting Started
    1. Effective Async Technical Discussions
    2. Effective Async Technical Project Management
    3. Cloud Onboarding for AWS, GCP, and Azure (1/4)
    4. Cloud Onboarding for AWS, GCP, and Azure (2/4)
    5. Cloud Onboarding for AWS, GCP, and Azure (3/4)
    6. Cloud Onboarding for AWS, GCP, and Azure (4/4)
  4. 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 (1/3)
    4. PaaS Continuous Delivery (2/3)
    5. PaaS Continuous Delivery (3/3)
    6. IaC (Infrastructure as Code)
    7. What is Continuous Delivery and Continuous Deployment?
    8. Continuous Delivery for Hugo Static Site from Zero
  5. Chapter3: Virtualization & Containerization & Elasticity
    1. Elastic Resources
    2. Containers: Docker
    3. Container Registries (1/2)
    4. Container Registries (2/2)
    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 (1/2)
    9. Operationalizing a Microservice Overview (2/2)
    10. Creating a Locust Load test with Flask
    11. Serverless Best Practices, Disaster Recovery and Backups for Microservices
  6. 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 (1/2)
    8. ASICS: GPUs, TPUs, FPGA (2/2)
  7. Chapter 5: Cloud Storage
    1. Cloud Storage Types
    2. Data Governance
    3. Cloud Databases
    4. Key-Value Databases
    5. Graph Databases (1/3)
    6. Graph Databases (2/3)
    7. Graph Databases (3/3)
    8. Batch vs. Streaming Data and Machine Learning
    9. Cloud Data Warehouse
    10. GCP BigQuery
    11. AWS Redshift
  8. Chapter 6: Serverless ETL Technologies
    1. AWS Lambda
    2. Developing AWS Lambda Functions with AWS Cloud9
    3. Faas (Function as a Service)
    4. Chalice Framework on AWS Lambda
    5. Google Cloud Functions (1/3)
    6. Google Cloud Functions (2/3)
    7. Google Cloud Functions (3/3)
    8. To run it locally, follow these steps
    9. Cloud ETL
    10. Real-World Problems with ETL Building a Social Network From Scratch (1/2)
    11. Real-World Problems with ETL Building a Social Network From Scratch (2/2)
  9. Chapter 07: Managed Machine Learning Systems
    1. Jupyter Notebook Workflow
    2. AWS Sagemaker Overview
    3. AWS Sagemaker Elastic Architecture
    4. Azure ML Studio Overview
    5. Google AutoML Computer Vision
  10. Chapter 08: Data Science Case Studies and Projects
    1. Case Study: Data science meets Intermittent Fasting (IF)
  11. Chapter 09: 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 (1/2)
    7. One Million Trained by 2021 (2/2)
  12. Chapter 10: Career
    1. Getting a job by becoming a Triple Threat
    2. How to Build a Portfolio for Data Science and Machine Learning Engineering
    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

Product information

  • Title: Cloud Computing for Data Analysis
  • Author(s): Noah Gift, Alfredo Deza
  • Release date: December 2020
  • Publisher(s): Pragmatic AI Labs
  • ISBN: None