Cloud Computing for Data Analysis
Cloud Computing for Data Analysis
The missing semester of Data Science
About the Book
After reading this book you will be able to:
- Summarize the fundamentals of cloud computing
- Evaluate the economics of cloud computing
- Accurately evaluate distributed computing challenges and opportunities and apply this knowledge to real-world projects.
- Develop non-linear life-long learning skills
- Build, share and present compelling portfolios using: Github, YouTube, and Linkedin.
About the Author
Bundles that include this book
Table of Contents
-
Introduction
- About the Cover
- What you will learn
-
Chapter One: Getting Started
- Effective Async Technical Discussions
- Effective Async Technical Project Management
- Cloud Onboarding for AWS, GCP, and Azure
-
Chapter 2: Cloud Computing Foundations
- Why you should consider using a cloud-based development environment
- Overview of Cloud Computing
- PaaS Continuous Delivery
- IaC (Infrastructure as Code)
- What is Continuous Delivery and Continuous Deployment?
- Continuous Delivery for Hugo Static Site from Zero
-
Chapter3: Virtualization & Containerization & Elasticity
- Elastic Resources
- Containers: Docker
- Container Registries
- Kubernetes in the Cloud
- Hybrid and Multi-cloud Kubernetes
- Running Kubernetes locally with Docker Desktop and sklearn flask
- Operationalizing a Microservice Overview
- Creating a Locust Load test with Flask
- Serverless Best Practices, Disaster Recovery and Backups for Microservices
-
Chapter 4: Challenges and Opportunities in Distributed Computing
- Eventual Consistency
- CAP Theorem
- Amdahl’s Law
- Elasticity
- Highly Available
- End of Moore’s Law
- ASICS: GPUs, TPUs, FPGA
-
Chapter 5: Cloud Storage
- Cloud Storage Types
- Data Governance
- Cloud Databases
- Key-Value Databases
- Graph Databases
- Batch vs. Streaming Data and Machine Learning
- Cloud Data Warehouse
- GCP BigQuery
- AWS Redshift
-
Chapter 6: Serverless ETL Technologies
- AWS Lambda
- Developing AWS Lambda Functions with AWS Cloud9
- Faas (Function as a Service)
- Chalice Framework on AWS Lambda
- Google Cloud Functions
- To run it locally, follow these steps
- Cloud ETL
- Real-World Problems with ETL Building a Social Network From Scratch
-
Chapter 07: Managed Machine Learning Systems
- Jupyter Notebook Workflow
- AWS Sagemaker Overview
- AWS Sagemaker Elastic Architecture
- Azure ML Studio Overview
- Google AutoML Computer Vision
-
Chapter 08: Data Science Case Studies and Projects
- Case Study: Data science meets Intermittent Fasting (IF)
-
Chapter 09: Essays
- Why There Will Be No Data Science Job Titles By 2029
- Exploiting The Unbundling Of Education
- How Vertically Integrated AI Stacks Will Affect IT Organizations
- Here Come The Notebooks
- Cloud Native Machine Learning And AI
- One Million Trained by 2021
-
Chapter 10: Career
- Getting a job by becoming a Triple Threat
- How to Build a Portfolio for Data Science and Machine Learning Engineering
- How to learn
- Create your own 20% Time
- Pear Revenue Strategy
- Remote First (Mastering Async Work)
- Getting a Job: Don’t Storm the Castle, Walk in the backdoor
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