Schedule and Calendar

Overall schedule can be found here and calendar here.

Week 1 - Introduction, Virtual Environments and Virtual Machines

Sep 05
Introduction
Lecture 1 ,   Setup & Installation
Sep 07
Virtual Environments and Virtual Machines
Lecture 2

Week 2 - Containers

Sep 12
Containers I
Lecture 3
Sep 14
Containers II
Lecture 4
M 1 due

Week 3 - Data

Sep 19
Data Pipelines: Extract, Transform, Labeling, Versioning
Lecture 5
Sep 21
Dask
Lecture 6

Week 4 - Data and Models

Sep 26
TF Data and TF Records
Lecture 7
M 2 due
Sep 28
Advanced training workflows: experiment tracking (W&B), multi GPU, serverless training (Vertex AI), kubeflow
Lecture 8

Week 5 - Project Week

Oct 3
No class (Project Week)
Oct 5
No class (Project Week) M 3 due

Week 6 - Model

Oct 10
Distillation/Quantization/Compression, TF lite
Lecture 9
Oct 12
Model performance monitoring, data drift, or other post release items to be aware of
Lecture 10

Week 7 - ML Workflow Management

Oct 17
Cloud functions, Cloud Run, Vertex AI Pipelines
Lecture 11
Oct 19
Hands on Mushroom App Pipelines
Lecture 12

Week 8 - Midterm presentations

Oct 24
Midterm presentations (See Ed)
M 4 due
Oct 26
Midterm presentations (See Ed)
M 4 due

Week 9 - App Development

Oct 31
App design, setup and code organization
Lecture 13
Nov 2
APIs & Frontend
Lecture 14

Week 10 - Deployment & Scaling

Nov 7
Deployment
Lecture 15
Nov 9
Scaling: Kubernetes
Lecture 16

Week 11 - Frontend

Nov 14
React Session (Online)
Lecture 17
Nov 16
Projects

Week 12 - Thanks giving

Nov 20

M 5 due

Week 13 - Automation

Nov 28
Automation: GitHub Actions
Lecture 18
Nov 30
Projects

Week 14 - Projects

Week 15 - Presentations

Dec 12
Final Project Submission M 6 due

Setup & Installation

Refer to the setup and installtion document for a full list of softwares and tools we will be using in this class

Policy on Usage of Publicly Available Class Material

  1. Permitted Use: Class Material is made available primarily for the educational benefit of enrolled students and may be used by others for personal educational purposes only.

  2. Prohibited Use:
    • Selling or commercializing any part of the Class Material.
    • Sharing, distributing, or publishing any part of the Class Material in any form or through any medium without explicit permission from the instructor.
    • Modifying or altering the Class Material to create derivative works.

  3. Attribution: Any permitted use of the Class Material must carry appropriate acknowledgment of the source (e.g., the instructor’s name, course title, and institution).

  4. Enforcement: Failure to comply with this policy may result in legal action and/or disciplinary measures as applicable.

By accessing and using the Class Material, you indicate your acknowledgment and acceptance of this policy.