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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. How do I develop my body of work?

ML 52
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Azure service cloud summarized: Part I

Mlearning.ai

Learning about the framework of a service cloud platform is time consuming and frustrating because there is a lot of new information from many different computing fields (computer science/database, software engineering/developers, data science/scientific engineering & computing/research). Data Factory 2.

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Data Science Career FAQs Answered: Educational Background

Mlearning.ai

Familiarity with libraries like pandas, NumPy, and SQL for data handling is important. In spite of all this, over the next few years I do expect the requirement for entry-level DS/ML roles to go down, as it did with SDE-role. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).

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How to become a data scientist

Dataconomy

To put it another way, a data scientist turns raw data into meaningful information using various techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. Machine learning Machine learning is a key part of data science.

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Top NLP Skills, Frameworks, Platforms, and Languages for 2023

ODSC - Open Data Science

Data Science Fundamentals Going beyond knowing machine learning as a core skill, knowing programming and computer science basics will show that you have a solid foundation in the field. Computer science, math, statistics, programming, and software development are all skills required in NLP projects.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

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