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

Flipboard

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

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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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Data Science Career FAQs Answered: Educational Background

Mlearning.ai

Familiarity with libraries like pandas, NumPy, and SQL for data handling is important. Check out this course to upskill on Apache Spark —  [link] Cloud Computing technologies such as AWS, GCP, Azure will also be a plus. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. These models may include regression, classification, clustering, and more.

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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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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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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

Mikiko Bazeley: Most people are really surprised to hear that my background in college was not computer science. You see them all the time with a headline like: “data science, machine learning, Java, Python, SQL, or blockchain, computer vision.” For example, you can use BigQuery , AWS , or Azure.

ML 52