Schedule and Calendar
Overall schedule can be found here and calendar here.
Week 1 - Introduction, Virtual Environments, Virtual Machines
- Sep 03
- Introduction
- Lecture 1 , Setup & Installation
- Sep 05
- Virtual Enviroments and Virtual Machines
- Lecture 2
Week 2 - Containers
Week 3 - Data Pipelines
Week 4 - LLM Tools and Agents
- Sep 24
- LLM tools and agents: RAG I
- Lecture 7
- Sep 26
- LLM tools and agents: RAG II
- Lecture 8 , Extra-RAG Eval
Week 5 - Fine Tuning, LORA
- Oct 1
- LLM fine tuning and LORA - I
- Lecture 9
- Oct 3
- LLM fine tuning and LORA - II
- Lecture 10
Week 6 - Project Week
- Oct 8
- Project
- Oct 10
- Project
Week 7 - Model Compression and Distillation, Advanced Training Workflows
- Oct 15
- Model Compression and Distillation
- Lecture 11
- Oct 17
- Advanced training workflows: experiment tracking (W&B), multi GPU, serverless training (Vertex AI)
- Lecture 12
Week 8 - Cloud Function and Cloud Run, Guest Lecture
- Oct 22
- Cloud Function and Cloud Run
- Lecture 13
- Oct 24
- Modal Labs - Guest Lecture
- Lecture 14
Week 9 - ML Workflows with Vertex AI, Midterm
- Oct 29
- ML Workflows with Vertex AI
- Lecture 15
- Oct 31
- Midterm (M3) Presentations
- M3 due 10/31
Week 10 - Github Actions, App Development
- Nov 5
- Automating Software Development: CI/CD with GitHub Actions and other tools
- Lecture 16
- Nov 7
- App design, setup and code organization
- Lecture 17
Week 11 - APIs & Frontend, Ansible
- Nov 12
- APIs & Frontend
- Lecture 18
- Nov 14
- Deployment: Ansible
- Lecture 19
Week 12 - Scaling Kubernetes, CI CD
- Nov 19
- Scaling: Kubernetes
- Lecture 20
- Nov 21
- Final: CI/CD releases
- Lecture 21
Week 13 - Thanksgiving
- Nov 26
- Thanksgiving Week
- Nov 28
- Thanksgiving Week
Week 14 - Projects
- Dec 3
- Project
Week 15 - Projects
- Dec 9
- Project Showcase
- Dec 11
- Project Deliverables Due
Setup & Installation
Refer to the setup and installation document for a full list of softwares and tools we will be using in this class
Policy on Usage of Publicly Available Class Material
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.
- 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.
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).
- Enforcement: Failure to comply with this policy may result in legal action and/or disciplinary measures as applicable.
Consent:
By accessing and using the Class Material, you indicate your acknowledgment and acceptance of this policy.