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Bartosz Mikulski
Building trustworthy data pipelines because AI cannot learn from dirty data
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Check-Engine - data quality validation for PySpark 3.0.0
Last week, I was testing whether we can use AWS Deequ for data quality validation. I ran into a few problems. First of all, it...
The problem with software testing in data engineering
What if we found a bug in our data pipelines? What if that bug were easy to fix, but it would require a lot of...
Data flow - what functional programming and Unix philosophy can teach us about data streaming
What does stream processing have in common with functional programming and Unix?
Four books to boost your programmer career
I quit my dream job because of a book
All Stories
Building and deploying ML models using Qwak ML platform
What is Qwak ML platform and how does it work?
How to learn TDD
Learning Test-Driven Development is hard and there is nothing we can do about it
Data Engineering - the first principles
What is true in every data engineering project?
How to deploy MLFlow on Heroku
How to deploy MLFlow on Heroku using PostgreSQL as the database, S3 as the artifact storage and with BasicAuth authentication
What is MLOps? A quite controversial definition
MLOps is not just DevOps applied to machine learning!
How to add a new dataset to the Feast feature store
How to use Feast feature store in a local environment
Building trustworthy data pipelines
How to build a trustworthy data pipeline?
Theory of constraints in data engineering
Are you busy, but nothing ever gets done? Perhaps, theory of constraints will help you
How writing can improve your programming skills
How writing texts for people makes you a better programmer
The ugly truth about product demo storytelling in data teams
How to make product demos more engaging and persuade people to care about the data
Multimodel deployment in Sagemaker Endpoints
How to deploy multiple models in a single Sagemaker Endpoint?
How to speed up Pandas?
Is the Pandas library too slow? Here are two methods to speed it up!
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