Course Logistics
Online Learning Details
Schedule
Project
Lecture 1
1.
Basics
2.
SSH and Firewall
3.
Setting up Python
4.
Remote Jupyter Server
5.
Recommendation Models
Recommendation (SVD) Training
Recommendation (SVD) Inference
Recommendation (Pytorch) Training
Recommendation (Pytorch) Inference
6.
Serving ML Models Using Web Servers
7.
Flask App
Exercises
Lecture 2
1.
Serverless Deployments
2.
Cloud Functions
3.
GCP Serverless Model Serving
4.
Lambda Functions
5.
AWS Serverless Model Serving
Exercises
Lecture 3
1.
Introduction
2.
Docker
3.
Orchestration using ECS and ECR - Part I
4.
Orchestration using ECS and ECR - Part II
Exercises
Lecture 4
1.
Kubernetes
2.
Model Serving using Kubernetes
3.
Orchestration using GKE
Exercises
Lecture 5
1.
Data Science Workflows
2.
Training Workflows
3.
Cron Jobs
4.
Apache Airflow
Exercises
Lecture 6
1.
Spark based Pipelines
2.
Spark Clusters
3.
Spark on Databricks
4.
PySpark
5.
MLlib - ML Library for Spark
Exercises
Lecture 7
1.
Streaming Workflows
2.
Apache Kafka
3.
Spark Streaming
Exercises
Lecture 8
1.
A/B Testing
2.
Statistical Tests
3.
Testing Models
GitHub repo
Recommendation Models
We will look at two models for recommending movies to existing users.
Matrix factorization based on the surprise package.
Matrix factorization based on Pytorch.