Cloud Computing for Data Analysis
Abstract
A practical guide to Data Science, Machine Learning Engineering and Data Engineering
Abstract
This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. A variety of learning 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.
TOC (Table of Contents) Book
Chapter 1: Getting Started
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
- CPU, Memory, I/O
- 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 Loadtest with Flask
- Serverless Best Practices, Disaster Recovery and Backups for Microservices
Chapter 4: Challenges and Opportunities in Distributed Computing
- CAP Theorem
- CAP Theorem
Chapter 5: Cloud Storage
- Cloud Databases: HBase, MongoDB, Cassandra, DynamoDB, Google BigQuery
Chapter 6: Serverless
- AWS Cloud 9 Development Environment
- FaaS (Function as a Service)
- AWS Lambda
- GCP Cloud Functions
- Azure Functions
- AWS Cloud-Native Primitives Overview
- AWS Step Machines
- AWS SQS
- AWS SNS
- AWS Cognito
- AWS API Gateway
- Google Cloud Shell Development Environment
- Google App Engine
Chapter7: Big Data Platforms
- Batch Processing: EMR/Hadoop, AWS Batch
Chapter 8: Managed Machine Learning Systems, Platforms and AutoML
- AutoML Overview
- AWS Sagemaker
- AWS Sagemaker Autopilot
- GCP AI Platform
- GCP AutoML Overview
- GCP AutoML Vision
- GCP AutoML Tables
- Azure ML Studio
- H20 AutoML
- Open Source ML Platforms Overview
- Ludwig
Chapter9: Edge Computing
- IoT Overview
- AWS Greengrass
- Raspberry Pi
- Edge Machine Learning Solutions Overview
- Google AutoML
- Tensorflow lite
- Intel Movidius
- Apple X12
Chapter 10: Data Science Case Studies and Projects
- Case Study: Datascience meets intermittent fasting
- Case Study: Coronavirus Epidemic
- Applied Computer Vision Overview
- Project: AWS DeepLense Edge Computer Vision
- Project: Rasberry Pi
- Project: Intel Movidius Edge Computer Vision
- Project: Serverless Data Engineering Pipelines
- Project: Operationalizing Containerized Machine Learning Models
- Project: Continuous Delivery of GCP PaaS
- Project: Using Docker Containers and Registeries
- Project: Cloud Machine Learning with Kubernetes
Chapter 11: 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
- The “missing technical sememester” for MBA programs
Chapter 12: Cloud Certifications
- AWS Certification Guide Overview
- AWS Certified Cloud Practitioner
- AWS Certified Solutions Architect
- AWS Certified Developer
- AWS Certified Data Analytics Specialty
- AWS Certified Machine Learning Specialty
- GCP Certification Guide Overview
- Azure Certification Guide Overview
Chapter 13: Career
- Getting a job by becoming a Triple Threat
- How to build a Portfolio
- How to learn
- Pear Revenue Strategy