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How Zalando optimized large-scale inference and streamlined ML operations on Amazon SageMaker

AWS Machine Learning Blog

Data collection – Updated shop prices lead to updated demand. The new information is used to enhance the training sets used in Step 1 for forecasting discounts. To improve forecasting accuracy, all involved ML models need to be retrained, and predictions need to be produced weekly, and in some cases daily.

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Boosting Resiliency with an ML-based Telemetry Analytics Architecture | Amazon Web Services

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Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Skip Levens, Marketing Director, Media & Entertainment, Quantum – Interview Series

Unite.AI

Quantum provides end-to-end data solutions that help organizations manage, enrich, and protect unstructured data, such as video and audio files, at scale. Their technology focuses on transforming data into valuable insights, enabling businesses to extract value and make informed decisions.

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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning Blog

Deltek serves over 30,000 clients with industry-specific software and information solutions. Deltek is continuously working on enhancing this solution to better align it with their specific requirements, such as supporting file formats beyond PDF and implementing more cost-effective approaches for their data ingestion pipeline.

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Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.

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Use GitHub Actions with Azure ML Studio: train, deploy/publish, monitor

Mlearning.ai

I highly recommend anyone coming from a Machine Learning or Deep Learning modeling background who wants to learn about deploying models (MLOps) on a cloud platform to take this exam or an equivalent; the exam also includes topics on SQL data ingestion with Azure and Databricks, which is also a very important skill to have in Data Science.

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