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Graceful External Termination: Handling Pod Deletions in Kubernetes Data Ingestion and Streaming…

IBM Data Science in Practice

Graceful External Termination: Handling Pod Deletions in Kubernetes Data Ingestion and Streaming Jobs When running big-data pipelines in Kubernetes, especially streaming jobs, its easy to overlook how these jobs deal with termination. If not handled correctly, this can lead to locks, data issues, and a negative user experience.

Python 130
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Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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Cookiecutter Data Science V2

DrivenData Labs

Hello from our new, friendly, welcoming, definitely not an AI overlord cookie logo! This better reflects the common Python practice of having your top level module be the project name. Ruff is also emerging as a great all-purpose formatter and linter for Python codebases and may be an option in later CCDS versions.

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14 Datasets for Economics to Help Find and Use Data for Powerful Insights

ODSC - Open Data Science

Dataforge-Economics: A resource built to train models in economic literacy, offering definitions, concepts, and structured insights for educational or modeling use. Integration with Excel, Python (fredapi), and R. Our World in Data : Research-backed datasets on global issues — poverty, energy, climate, and more.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

.” — Conor Murphy , Lead Data Scientist at Databricks, in “Survey of Production ML Tech Stacks” at the Data+AI Summit 2022 Your team should be motivated by MLOps to show everything that goes into making a machine learning model, from getting the data to deploying and monitoring the model. AIIA MLOps blueprints.

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Fine tune a generative AI application for Amazon Bedrock using Amazon SageMaker Pipeline decorators

AWS Machine Learning Blog

This makes managing and deploying these updates across a large-scale deployment pipeline while providing consistency and minimizing downtime a significant undertaking. Generative AI applications require continuous ingestion, preprocessing, and formatting of vast amounts of data from various sources. We use Python to do this.

ML 131
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Journeying into the realms of ML engineers and data scientists

Dataconomy

With their technical expertise and proficiency in programming and engineering, they bridge the gap between data science and software engineering. Programming skills: Data scientists should be proficient in programming languages such as Python, R, or SQL to manipulate and analyze data, automate processes, and develop statistical models.