Table of Contents
๐ I am Aurimas. I have now written and sent out almost 40 SwirlAI Newsletter episodes.
The value of most of the content in this Newsletter is meant to be timeless - the content can be read long after the episodes are released. However, until now it was complicated to find topics of interest as they are scattered throughout newsletter issues with no clear ordering or grouping. Hence, I built a Table of Contents that I will be updating and curating.
The amount of content really surprised me. There are more than 90 topics covered supplemented by a infographic drawn in my style!
The SwirlAI Data Engineering Project Master Template.
๐ Introduction
Data Engineering
Fundamentals
๐ Encoding in Parquet
Spark
๐ Architecture
๐ Shuffle
๐ Parallelism
๐ Caching
Kafka
๐ Use Cases
๐ Writing Data
๐ Reading Data
๐ Data Replication
๐ Reliability
Stream Processing
General
MLOps
Deployment
Infrastructure
๐ Feature Store
๐ Feature Platforms
Building efficient Experimentation Environments for ML Projects
Observability
Processes
Maturity Levels
System Design
Systems Thinking
๐ ML Systems in the context of end-to-end Product Delivery
๐ Why you should learn Data Engineering and Machine Learning Engineering Pipelines
Building Organisations
Containers and Kubernetes
Career
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