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The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context. Solution overview The solution integrates with your existing data catalogs and repositories, creating a unified, scalable semantic layer across the entire data landscape.
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.
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?
Stefan is a software engineer, data scientist, and has been doing work as an MLengineer. He also ran the dataplatform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the MLdataplatform team, how do you do that?
I see so many of these job seekers, especially on the MLOps side or the MLengineer 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.
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 MLengineers to build and deploy models at scale.
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