Building trustworthy data pipelines because AI cannot learn from dirty data
Why should you use a feature store
Benefits of having a feature store and what happens when you don't have one
18 Feb 2022
Data pipeline documentation without wasting your time
How to document an ETL pipeline or ML inference pipeline without doing useless work
11 Feb 2022
How to run batch inference using Sagemaker Batch Transform Jobs
Running a batch machine learning job using Sagemaker and data stored in S3.
04 Feb 2022
How to build maintainable software by abstracting the business rules in data engineering
Are we building the right abstractions?
28 Jan 2022
Testing legacy data pipelines
Do you struggle with maintaining your legacy data pipelines? Check out our article on how to add tests and refactor your code while working with legacy data pipelines.
21 Jan 2022
Secrets of mentoring junior software engineers
How to quickly train junior engineers to make them as productive as the rest of the team
14 Jan 2022
What does your data pipeline need in production?
When you're debugging a failing production pipeline at 2 am, what do you need?
07 Jan 2022
How to pass a machine learning engineer interview
Trivial (and easily fixable) mistakes that will make you fail a job interview
31 Dec 2021
Why do data engineers quit?
Why do data engineers quit their jobs?
24 Dec 2021
What is the essential KPI of an MLOps team?
What KPI to measure in an MLOps team
17 Dec 2021
Deploying your first ML model in production
The minimal setup for ML deployment without the things you DON'T need yet
10 Dec 2021
Is it overengineered?
What's the difference between reasonable future-proof architecture and overengineering? Is there a difference?
04 Dec 2021