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Data Transformation and Feature Engineering: Exploring 6 Key MLOps Questions using AWS SageMaker

Towards AI

This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. Automation] How can the transformation steps be applied in real-time to the live data before inference? To prepare the data for models, a data scientist often needs to transform, clean, and enrich the dataset.

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

Databricks: Powered by Apache Spark, Databricks is a unified data processing and analytics platform, facilitates data preparation, can be used for integration with LLMs, and performance optimization for complex prompt engineering tasks. Kubernetes: A long-established tool for containerized apps.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPI s (Key Performance Indicators) for making any further calls. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

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How to Use Exploratory Notebooks [Best Practices]

The MLOps Blog

Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas data wrangling, or create plots is not important for readers. in a pandas DataFrame) but in the company’s data warehouse (e.g., documentation.

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