Remove category engineering data-warehousing
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Why optimize your warehouse with a data lakehouse strategy

IBM Journey to AI blog

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

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How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch

AWS Machine Learning Blog

In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%. An effective solution to this problem is the prediction of product categories. SageMaker is a fully managed ML service.

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Demand forecasting at Getir built with Amazon Forecast

AWS Machine Learning Blog

Solution overview Six people from Getir’s data science team and infrastructure team worked together on this project. Next, a feature processing job prepares daily features stored in Amazon Redshift and unloads the time series data to Amazon Simple Storage Service (Amazon S3). The following diagram shows the solution’s architecture.