Remove Data Engineering Remove Data Modeling Remove EDA
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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. Read more to know.

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Introducing our New Book: Implementing MLOps in the Enterprise

Iguazio

Who This Book Is For This book is for practitioners in charge of building, managing, maintaining, and operationalizing the ML process end to end: Data science / AI / ML leaders: Heads of Data Science, VPs of Advanced Analytics, AI Lead etc. Exploratory data analysis (EDA) and modeling.

ML 52
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ML Collaboration: Best Practices From 4 ML Teams

The MLOps Blog

Data scientists frame the business problem and the objective into a statistical solution and start with the very first step of data exploration. EDA, as it is popularly called, is the pivotal phase of the project where discoveries are made. Approvals from stakeholders ML projects are inherently iterative by nature.

ML 78
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Generative AI in Software Development

Mlearning.ai

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze.

AI 52