Marktechpost
JUNE 13, 2024
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.
AWS Machine Learning Blog
MARCH 22, 2023
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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AWS Machine Learning Blog
NOVEMBER 15, 2023
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.
ODSC - Open Data Science
OCTOBER 7, 2024
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.
AWS Machine Learning Blog
NOVEMBER 22, 2023
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.
AWS Machine Learning Blog
JUNE 27, 2023
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.
AWS Machine Learning Blog
AUGUST 21, 2024
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.
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