# FSDL 2022 ## ML Lifecycle - Problem formulation, data, training ## Data Management - Feature stores, labeling, DVC ## Continual Learning - Monitoring, drift detection, retraining ## Infrastructure - Testing, deployment, observability

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