Cloud Computing for Data Analysis
Cloud Computing for Data Analysis
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Cloud Computing for Data Analysis

This book is 65% complete

Last updated on 2020-07-01

About the Book

After reading this book you will be able to:

  1. Summarize the fundamentals of cloud computing
  2. Evaluate the economics of cloud computing
  3. Accurately evaluate distributed computing challenges and opportunities and apply this knowledge to real-world projects.
  4. Develop non-linear life-long learning skills
  5. Build, share and present compelling portfolios using: Github, YouTube, and Linkedin.
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About the Author

Noah Gift
Noah Gift

Noah Gift lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, and the Graduate Data Science program at UC Berkeley and the UC Davis Graduate School of Management MSBA program, and UNC Charlotte Data Science Initiative. He is teaching and designing graduate machine learning, A.I., Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. 

Noah is a Python Software Foundation Fellow.  He currently holds the following industry certifications for AWS:  AWS Subject Matter Expert (SME) on Machine LearningAWS Certified Solutions Architect, and AWS Certified Machine Learning SpecialistAWS Certified Big Data Specialist, AWS Academy Accredited Instructor, AWS Faculty Ambassador.  He also is certified on both the Google and Azure platform: Google Certified Professional Cloud ArchitectCertified Microsoft MTA on Python. He has published over 100 technical publications including multiple books on subjects ranging from Cloud Machine Learning to DevOps. Publications appear in Forbes, IBM, Red Hat, Microsoft, O'Reilly, Pearson, Udacity, Coursera, datascience.com, and DataCamp. Workshops and Talks around the world for organizations including NASA, PayPal, PyCon, Strata, O'Reilly Software Architecture Conference, and FooCamp. As an SME on Machine Learning for AWS, he helped created the AWS Machine Learning certification.

He has worked in roles ranging from CTO, General Manager, Consulting CTO, Consulting Chief Data Scientist, and Cloud Architect. This experience has been with a wide variety of companies: ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab, and industries: Television, Film, Games, SaaS, Sports, Telecommunications. He has film credits in many major motion pictures for technical work, including Avatar, Spider-Man 3, and Superman Returns.

He has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had a global scale. Currently, he is consulting startups and other companies, on Machine Learning, Cloud Architecture, and CTO level consulting as the founder of Pragmatic A.I. Labs.

His most recent books are:

His most recent video courses are:

His most recent online courses are:

You can follow Noah Gift on social media and on the web at:

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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
    • Steps to run this project
  • 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) w/ Terraform
    • 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
    • Eventual Consistency
    • CAP Theorem
    • Amdahl’s Law
    • Elasticity
    • Highly Available
    • End of Moore’s Law
    • ASICS: GPUs, TPUs, FPGA
  • Chapter 5: Cloud Storage
    • Data Governance
    • Cloud Databases
    • Key Value Databases
    • Graph Databases
    • Cloud Object Storage: Amazon S3, GCP Cloud Storage, Amazon Glacier, Data Lakes, OpenStack Swift
    • Amazon S3
    • Batch vs Streaming Data and Machine Learning
    • Cloud Data Warehouse
    • GCP Big Query
    • AWS Redshift
    • Distributed File Systems: Red Hat Ceph, Amazon EFS (Elastic File System), HDFS
  • Chapter 6: Serverless
    • AWS Lambda
    • Developing AWS Lambda Functions with AWS Cloud9
    • AWS Step Functions
    • Building a serverless data engineering pipeline
    • Faas (Function as a Service)
    • Chalice Framework on AWS Lambda
    • Serverless
    • Google Cloud Functions
    • Kubernetes FaaS with GKE
    • Azure Functions
    • AWS Cloud-Native Primitives Overview
    • AWS SQS
    • AWS SNS
    • AWS Cognito
    • AWS API Gateway
    • Google Cloud Shell Development Environment
    • Google App Engine
  • Chapter7: Big Data Platforms
    • Cloud Object Storage
    • Amazon S3
    • Batch vs Streaming Data and Machine Learning
    • Batch Processing: EMR/Hadoop, AWS Batch
    • Cloud ETL
    • Real-World Problems with ETL Building a Social Network From Scratch
    • Stream Processing: EMR/Spark, AWS Kinesis, Kafka
  • Chapter 8: Managed Machine Learning Systems, Platforms and AutoML
    • Jupyter Notebook Workflow
    • AutoML Overview
    • AWS Sagemaker Overview
    • AWS Sagemaker Elastic Architecture
    • 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
    • One Million Trained by 2021
    • GI versus NoGi Brazilian Jiu-Jitsu
    • Do They Know What Good Is?
  • 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 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
    • Motivation: Four WHATS
  • Chapter 14: Machine Learning Engineering
    • What does an Machine Learning Engineer Do?
    • Machine Learning Engineering Overview
    • MLOps
    • AutoML for the ML Engineer
    • Data Engineering for Machine Learning
    • Apple Machine Learning Engineering
    • Amazon AWS Machine Learning Engineering
    • Google GCP Machine Learning Engineering
    • Microsoft Azure Machine Learning Engineering
  • Key Terms and Industry Jargon
    • Build Server
    • Microservice
    • FaaS (Function as a Service)
    • AWS Lambda
    • Cloud-Native applications
    • SQS Queue
    • Serverless
    • Moore’s Law
    • AWS Cloud9
    • Python Virtual Environment
    • Container
    • Virtual Machine
    • Docker Format Container
    • pip
    • pylint
    • black
    • pytest
    • IPython
    • Makefile
    • CircleCI
    • Docker
    • Amazon ECR
    • Swagger
    • Data Engineering
    • Ports
    • JSON
    • Kubernetes
    • Amazon EKS
    • Google GKE
    • Azure Kubernetes Service AKS
    • YAML
    • Kubernetes Pods
    • Kubernetes Containers
    • Kubernetes Clusters
    • Prometheus
    • Logging
    • Autoscaling
    • Alerts
    • Operationalization
    • Metrics
    • Disaster recovery
    • Migrate
    • Continuous Integration
    • Continuous Delivery
    • Load Testing
    • Locust

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