Machine Learning Systems Design
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Machine Learning Systems Design

Spring 2023, Group 1; Computer Engineering Department, Sharif University of Technology, Tehran, Tehran Province, Iran

Classes: Saturday and Monday, 16:30-18:00

The purpose of this course is to empower students to research in the development of a complete cycle of making an intelligent model for use in a commercial product. Unlike the classic course of machine learning, in this course the automation of this cycle, and its challenges in industrial applications is desired. This cycle includes all practical components such as data collection and labeling, data preprocessing, scalable model development, system evaluation, deployment, and system monitoring, and detecting and dealing with data distribution changes. In the end, the student is expected to be able to automatically launch and maintain one of these cycles in a real-world problem with the help of the tools and techniques introduced in the course, as well as the ability to research in the systemic fields of machine learning. get the

Syllabus


Instructors

Ali Zarezade

PhD

Tel +98 21 81901334

ali.zarezade@gmail.com

Teaching Assistants

Hossein Basafa
Lead TA

hossein.basafa.hb@gmail.com

Aryan Ahadinia

aryan.ahadinia@sharif.edu

Hossein Jafarnia

hussein.jafarinia@gmail.com

Ali Amiri

callmeamiri@gmail.com

Narges Javid

jatages@gmail.com

Omid Ghahroodi

oghahroodi98@gmail.com

Mostafa Ghadimi

mostafa.ghadimi@yahoo.com


Materials

Model Online Evaluation
Lecture 23

Slides
Model Maintenance
Lecture 22

Slides
Model Monitoring
Lecture 21

Slides
Deployment and Monitoring
Lecture 20

Slides
Model Serving (Cont.)
Lecture 19

Slides
Model Serving (Cont.)
Lecture 18

Slides
Model Serving
Lecture 17

Slides
Interpretability and Explainability
Lecture 16

Slides
High-performance Modeling
Lecture 15

Slides
Model Resource Management
Lecture 14

Slides
Hyperparameter tuning and AutoML
Lecture 13

Slides
Model Performance Analysis
Lecture 12

Slides
Model Development and Training (Cont.)
Lecture 11

Slides
Model Development and Training
Lecture 10

Slides
Feature Engineering (Cont.)
Lecture 9

Slides
Feature Engineering
Lecture 8

Slides
Data Preparation (Cont.)
Lecture 7

Slides
Data Preparation
Lecture 6

Slides
Data Engineering Fundamentals (Cont.)
Lecture 5

Slides
Data Engineering Fundamentals
Lecture 4

Slides
ML System Development Life Cycle
Lecture 3

Slides
Scoping the ML System Design Problem
Lecture 2

Slides
Understanding ML Systems
Lecture 1

Slides

Assignments

Models
HW2

Submission Deadline: 1402/02/11 23:59:59 with possibility of late submission

Question Set
Data
HW1

Submission Deadline: 1402/01/20 23:59:59 with possibility of late submission

Question Set