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LLMOps demystified: Why it’s crucial and best practices for 2023

Data Science Dojo

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

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Life of modern-day alchemists: What does a data scientist do?

Dataconomy

Today’s question is, “What does a data scientist do.” ” Step into the realm of data science, where numbers dance like fireflies and patterns emerge from the chaos of information. In this blog post, we’re embarking on a thrilling expedition to demystify the enigmatic role of data scientists.

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Exploratory Data Analysis: A Guide with Examples

Mlearning.ai

Before conducting any formal statistical analysis, it’s important to conduct exploratory data analysis (EDA) to better understand the data and identify any patterns or relationships. EDA is an approach that involves using graphical and numerical methods to summarize and visualize the data. Thank you for reading!

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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. The book contains a full chapter dedicated to generative AI. Key Takeaways 1.

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Turn the face of your business from chaos to clarity

Dataconomy

Integration also helps avoid duplication and redundancy of data, providing a comprehensive view of the information. Exploratory data analysis (EDA) Before preprocessing data, conducting exploratory data analysis is crucial to understand the dataset’s characteristics, identify patterns, detect outliers, and validate missing values.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

Data Science is a popular as well as vast field; till date, there are a lot of opportunities in this field, and most people, whether they are working professionals or students, everyone want a transition in data science because of its scope. How much to learn? What to do next?

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When his hobbies went on hiatus, this Kaggler made fighting COVID-19 with data his mission | A…

Kaggle

The early days of the effort were spent on EDA and exchanging ideas with other members of the community. Before models could be built, gaining an understanding of the data, strengths and weaknesses of the dataset and what researchers are looking for out of the CORD-19 dataset was needed.

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