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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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Turn the face of your business from chaos to clarity

Dataconomy

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Integration also helps avoid duplication and redundancy of data, providing a comprehensive view of the information.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building. These activities play a crucial role in extracting meaningful insights from raw data and improving model performance, leading to more robust and insightful results.

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Accelerate client success management through email classification with Hugging Face on Amazon SageMaker

AWS Machine Learning Blog

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

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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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Vertex AI: Guide to Google’s Unified Machine Learning Platform

Pickl AI

From data preparation and model training to deployment and management, Vertex AI provides the tools and infrastructure needed to build intelligent applications. This blog will delve into the world of Vertex AI, covering its overview, core components, advanced capabilities, real-world applications, best practices, and more.

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Introducing our New Book: Implementing MLOps in the Enterprise

Iguazio

There are 6 high-level steps in every MLOps project The 6 steps are: Initial data gathering (for exploration). Exploratory data analysis (EDA) and modeling. Data and model pipeline development (data preparation, training, evaluation, and so on).

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