---
layout: farshid_default
title: "FSDL 2022 Notes"
permalink: /notes/courses/full-stack-dl-2022/
description: "Notes from Full Stack Deep Learning 2022 covering ML lifecycle, data management, and continual learning."
---

Notes from Full Stack Deep Learning 2022 covering ML lifecycle, data management, and continual learning.

# Full Stack Deep Learning 2022

## Lecture 01: When to Use ML and Course Vision
- Formulating problems and estimating project cost
- Sourcing, cleaning, processing, labeling data
- Picking the right framework and compute
- Troubleshooting training and ensuring reproducibility
- Deploying the model at scale
- Monitoring and continually improving

## Lecture 02: Development Infrastructure & Tooling

## Lecture 03: Testing

## Lecture 04: Data Management
- Data sources: filesystem, object storage, database, data warehouse, data lake
- SQL and DataFrames (DASK, RAPIDS)
- Airflow, Prefect, Dagster
- Feature stores: Tecton, Feast, Featureform
- Labeling: Label Studio, Diffgram, Snorkel.ai
- Data versioning: DVC

## Lecture 06: Continual Learning
- Monitoring metrics: data quality, distribution drift
- System monitoring: Datadog, Honeycomb, NewRelic
- Data curation: random, stratified, active learning
- Retraining triggers and dataset formation
- Online testing: shadow mode, A/B testing

#FSDL #DeepLearning #MLOps #FarshidPirahansiah