Remove 2012 Remove Data Pipeline Remove Data Quality
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Feature Platforms?—?A New Paradigm in Machine Learning Operations (MLOps)

IBM Data Science in Practice

Hidden Technical Debt in Machine Learning Systems More money, more problems — Rise of too many ML tools 2012 vs 2023 — Source: Matt Turck People often believe that money is the solution to a problem. Tools like Git and Jenkins are not suited for managing data. This is where a feature platform comes in handy. Spark, Flink, etc.)

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Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

Flipboard

Amazon SageMaker Catalog serves as a central repository hub to store both technical and business catalog information of the data product. To establish trust between the data producers and data consumers, SageMaker Catalog also integrates the data quality metrics and data lineage events to track and drive transparency in data pipelines.

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Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

AWS Machine Learning Blog

Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. You can build and manage an incremental data pipeline to update embeddings on Vectorstore at scale.

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