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A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

This involves a series of semi-automated or automated operations implemented through data engineering pipeline frameworks. It provides components for data ingestion, validation, and feature extraction. Weaknesses: Steep learning curve, especially during initial setup.

ETL 128
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Build a machine learning model to predict student performance using Amazon SageMaker Canvas

AWS Machine Learning Blog

The label column name is Target, and it contains categorical data: dropout, enrolled, and graduate. Data ingestion The first step for any ML process is to ingest the data. We can use the outcome from the prediction to take proactive action to improve student performance and prevent potential dropouts.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

Combining accurate transcripts with Genesys CTR files, Principal could properly identify the speakers, categorize the calls into groups, analyze agent performance, identify upsell opportunities, and conduct additional machine learning (ML)-powered analytics.

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Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

Understanding the MLOps Lifecycle The MLOps lifecycle consists of several critical stages, each with its unique challenges: Data Ingestion: Collecting data from various sources and ensuring it’s available for analysis. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning.

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Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

AWS Machine Learning Blog

Tagging helps you categorize resources by purpose, team, environment, or other criteria relevant to your business. Cost attribution and analysis The process of categorizing costs is crucial in budgeting, accounting, financial reporting, decision-making, benchmarking, and project management.

IDP 112
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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning Blog

The ML components for data ingestion, preprocessing, and model training were available as disjointed Python scripts and notebooks, which required a lot of manual heavy lifting on the part of engineers. The initial solution also required the support of a technical third party, to release new models swiftly and efficiently.

DevOps 115
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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning Blog

The data scientist discovers and subscribes to data and ML resources, accesses the data from SageMaker Canvas, prepares the data, performs feature engineering, builds an ML model, and exports the model back to the Amazon DataZone catalog. The following diagram illustrates the workflow.