YUNNA WEI – Medium

YUNNA WEI

Published in
TDS Archive

Do you really need a Feature Store?

Feature Store — the interface between raw data and ML models

Mar 17, 2023

Do you really need a Feature Store?

Mar 17, 2023

Published in
Efficient Data+AI Stack

What constitutes an efficient development environment for data scientists?

The goal of building an efficient development environment for data scientists is more than assisting them to conduct PoC’s, it is also…

Mar 6, 2023

What constitutes an efficient development environment for data scientists?

Mar 6, 2023

MLOps Automation — CI/CD/CT for Machine Learning (ML) Pipelines

Scaling the use of AI/ML by building Continuous Integration (CI) / Continuous Delivery (CD) / Continuous Training (CT) pipelines for ML…

Feb 23, 2023

MLOps Automation — CI/CD/CT for Machine Learning (ML) Pipelines

Feb 23, 2023

Published in
TDS Archive

ML model registry — the “interface” that binds model experiments and model deployment

MLOps in Practice — A deep- dive into ML model registries, model versioning and model lifecycle management.

Feb 16, 2023

ML model registry — the “interface” that binds model experiments and model deployment

Feb 16, 2023

Published in
TDS Archive

Why Data Scientists Should Adopt Machine Learning (ML) Pipelines

MLOps in Practice — as a data scientist, are you handing over a notebook or an ML pipeline to your ML engineers or DevOps engineers for…

Feb 1, 2023

Why Data Scientists Should Adopt Machine Learning (ML) Pipelines

Feb 1, 2023

Published in
Efficient Data+AI Stack

MLOps in Practice — Machine Learning (ML) model deployment patterns (Part 1)

Machine Learning (ML) model serving and deployment is one of the most critical components of any solid ML solution architecture. This…

Jan 26, 2023

ML model deployment patterns

Jan 26, 2023

MLOps in Practice — Have you ever monitored your ML driven systems?

Monitoring plays a fundamental role in any solid ML solution architecture. It gives data scientists, ML engineers and system engineers the…

Jan 18, 2023

MLOps in Practice — Have you ever monitored your ML driven systems?

Jan 18, 2023

Published in
TDS Archive

Build low-latency and scalable ML model prediction pipelines using Spark Structured Streaming and…

MLOps in practice series — sharing design and implementation patterns of critical MLOps component. The focus of today’s article is on…

Jan 10, 2023

Build low-latency and scalable ML model prediction pipelines using Spark Structured Streaming and…

Jan 10, 2023

Published in
Efficient Data+AI Stack

Continuously ingest and load CSV files into Delta using Spark Structure Streaming

Leverage Spark Structure Streaming to efficiently ingest CSV files and load as Delta. Spark structure streaming provides the advantages of…

Jan 5, 2023

Continuously ingest and load CSV files into Delta using Spark Structure Streaming

Jan 5, 2023

Published in
TDS Archive

Have You Ever “Tested” Your Data Pipelines?

A comprehensive guide to make your data pipelines testable, maintainable and reliable

Dec 17, 2022

Have You Ever “Tested” Your Data Pipelines?

Dec 17, 2022

YUNNA WEI

YUNNA WEI

Write about modern data stack, MLOps implementation patterns and data engineering best practices. Let’s connect! https://www.linkedin.com/in/yunna-wei-64769a97/

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