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Most Common Use Cases of Data Engineering in Healthcare

phData

Data engineering in healthcare is taking a giant leap forward with rapid industrial development. Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords these days with developments of Chat-GPT, Bard, and Bing AI, among others. Data engineering can serve as the foundation for every data need within an organization.

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

Pickl AI

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

professionals

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Join DataHour Sessions With Industry Experts

Analytics Vidhya

Introduction Are you curious about the latest advancements in the data tech industry? Perhaps you’re hoping to advance your career or transition into this field. In that case, we invite you to check out DataHour, a series of webinars led by experts in the field.

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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning Blog

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. He holds a Ph.D.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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Data Analytics in the Age of AI, When to Use RAG, Examples of Data Visualization with D3 and Vega…

ODSC - Open Data Science

New Tool Thunder Hopes to Accelerate AI Development Thunder is a new compiler designed to turbocharge the training process for deep learning models within the PyTorch ecosystem. Learn more about them here! Be sure to check them out and try out some new platforms & services that just might be your company’s new secret weapon.

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Use Amazon SageMaker Canvas to build machine learning models using Parquet data from Amazon Athena and AWS Lake Formation

AWS Machine Learning Blog

One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. This means that business analysts who want to extract insights from the large volumes of data in their data warehouse must frequently use data stored in Parquet. Choose Join data.