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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. This approach is not novel.

IT 364
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Because Amazon DataZone integrates the data quality results, by subscribing to the data from Amazon DataZone, the teams can make sure that the data product meets consistent quality standards. Amazon DataZone empowers EUROGATE by setting the stage for long-term operational excellence and scalability.

IoT 111
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12 data science certifications that will pay off

CIO Business Intelligence

You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting. and SAS Text Analytics, Time Series, Experimentation, and Optimization.

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What is a DataOps Engineer?

DataKitchen

You might say that DataOps Engineers own the pipelines and the overall workflow, whereas data scientists and others work within the pipelines. DataOps is a set of practices, cultural norms and architecture patterns that help data professionals deliver value quickly. The data engineer builds data transformations.

Testing 152
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Deploy and Scale AI Applications With Cloudera AI Inference Service

Cloudera

By 2023, the focus shifted towards experimentation. Detailed Data and Model Lineage Tracking*: Ensures comprehensive tracking and documentation of data transformations and model lifecycle events, enhancing reproducibility and auditability. These innovations pushed the boundaries of what generative AI could achieve.

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How CFM built a well-governed and scalable data-engineering platform using Amazon EMR for financial features generation

AWS Big Data

The AWS pay-as-you-go model and the constant pace of innovation in data processing technologies enable CFM to maintain agility and facilitate a steady cadence of trials and experimentation. In this post, we share how we built a well-governed and scalable data engineering platform using Amazon EMR for financial features generation.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.