AWS Machine Learning Blog

OCTOBER 20, 2023

Data scientists search and pull features from the central feature store catalog, build models through experiments, and select the best model for promotion. Data scientists create and share new features into the central feature store catalog for reuse.

ML 101

ML Data Scientist ML Engineer Data Science 101

The MLOps Blog

JANUARY 26, 2024

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline? What is a feature store?

Machine Learning 73

Machine Learning Metadata ML Python 73

The MLOps Blog

OCTOBER 3, 2023

I see so many of these job seekers, especially on the MLOps side or the ML engineer side. You see them all the time with a headline like: “data science, machine learning, Java, Python, SQL, or blockchain, computer vision.” There’s no component that stores metadata about this feature store? It’s two things.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML data platform team, how do you do that?

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69