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Course Calendar 2022

Week 1

Cloud Computing Foundations-Part 1

Topics

  • Overview of Cloud Computing
  • Cloud Adoption Framework(s)
  • Economics of Cloud Computing
  • Develop non-linear life-long learning skills and metacognition

Readings/Media

Lab

Discussion

  • What skills are you going to learn by the end of this year, why and how?
  • Answer one of the Case Study Questions in Chapter 9-Cloud Computing

Demo

  • Create and share a 1-5 minute demo (hard capped at 5 minutes) “demo” video explaining your project plan. Looking at the requirements for the final individual project, create a week by week schedule for the next 10 weeks with specific milestones. Use the spreedsheet template as a reference. For each week, create a “weekly demo” slot. This is where you will screencast a 90 second demo of your project during this period.

Week 2

Cloud Computing Foundations-Part 2

Topics

  • Cloud Service Models: SaaS, PaaS, IaaS, MaaS, Serverless
  • Continuous Delivery

Readings/Media

Lab

Discussion

Demo

Week 3

Cloud Computing Foundations-Part 3

Topics

  • IaC (Infrastructure as Code)
  • DevOps Principles

Readings/Media

Lab

Discussion

Demo

Week 4

Cloud Virtualization, Containers and API: Virtualization and Containers

Topics

  • Evaluate different virtualization abstractions.
  • Build solutions with containers.
  • Build solutions with virtual machines.

Readings/Media

Lab

Discussion

Demo

Week 5

Cloud Virtualization, Containers and API: Microservices

Topics

  • Evaluate Microservice architectures.
  • Build Microservices with Python Flask and Python FastAPI.
  • Apply DevOps best practices for Serverless Microservices.

Readings/Media

Lab

Discussion

Demo

Week 6

Cloud Computing Foundations-Part 1

Topics

  • Build effective and actionable monitoring and alerting.
  • Evaluate different infrastructure configurations that optimize Cloud computing performance.
  • Evaluate best practices for Operations including alerts, load testing and Kaizen methodology.

Readings/Media

Lab

Discussion

Answer two Critical Thinking questions from [O'Reilly-Practical MLOps-Chapter 6-Monitoring and Logging](https://learning.oreilly.com/library/view/practical-mlops/9781098103002/ch06.html#idm45917447751352

Demo

Demo one of the exercises in O'Reilly-Practical MLOps-Chapter 6-Monitoring and Logging by applying to one of your individual projects.

Week 7

Cloud Data Engineering: Getting Started with Cloud Data Engineering

Topics

  • Explore the overall structure and final project goals of this course.
  • Evaluate best practices for dealing with the end of Moore's Law.
  • Develop distributed systems that apply software engineering best practices.
  • Explore Big Data Systems

Readings/Media

Lab

Discussion

Demo

  • Demo one of the big data systems as it applies to your individual project by running a job on a cluster: Snowflake, EMR/Spark, Databricks.

Week 8

Cloud Data Engineering: Examining Principles of Data Engineering

Topics

  • Analyze best practices in Data Engineering.
  • Build Python Command-line tools.
  • Apply software engineering best practices in testing to Command-line tools.

Readings/Media

Lab

Discussion

Answer two Critical Thinking questions from O'Reilly-Practical MLOps-Chapter 11-Building MLOps Command Line Tools and Microservices.

Demo

Week 9

Cloud Data Engineering: Building Data Engineering Pipelines

Topics

  • Build a serverless Data Engineering system.
  • Evaluate effective Data Governance in Cloud solutions.

Readings/Media

Lab

Discussion

  • What are three big advantages to serverless technology and how can you leverage this in your projects?

Demo

Week 10

Cloud Data Engineering: Applying Key Data Engineering Tasks

Topics

  • Develop Cloud ETL (Extract, Load, Transfer) pipelines.
  • Evaluate best practices for Cloud databases and storage.

Readings/Media

Lab

Discussion

  • When are advantages of serverless cloud databases like Google Big Query or AWS Athena for Data Engineering? How could incorporate these advantages into one of your project?

Demo

Week 11

MLOps: Cloud Machine Learning Engineering and MLOps

Topics

  • Explore the overall structure and final project goals of this course.
  • Evaluate machine learning engineering best practices.
  • Build machine learning applications.

Readings/Media

Lab

Discussion

Answer two Critical Thinking questions from O'Reilly-Practical MLOps-Chapter 2. MLOps Foundations.

Demo

Week 12

MLOps:Using AutoML

Topics

  • Develop Cloud solutions with AutoML.
  • Evaluate open source and proprietary AutoML.
  • Evaluate AutoML strategies with Ludwig.

Readings/Media

Lab

Discussion

Answer two Critical Thinking questions from O'Reilly-Practical MLOps-Chapter 5. AutoML and KaizenML.

Demo

Week 13

MLOps:Emerging Topics in Machine Learning

Topics

Readings/Media

Lab

Discussion

Answer two Critical Thinking questions from O'Reilly--Practical MLOps-Chapter 10. Machine Learning Interoperability.

Demo

Week 14

MLOps: Edge Computer Vision

Topics

  • Learn to build Applied Computer Vision MVPS
  • Learn to use transfer learning to solve Computer Vision Problems
  • Learn to use AutoML to solve Computer Vision Problems

Readings/Media

Lab

Discussion

Answer two Critical Thinking questions from O'Reilly-Practical MLOps-Chapter 12. Machine Learning Engineering and MLOps Case Studies.

Demo

Week 15

Review Entire Course

Week 16

  • Final Projects Demo