Remove Data Modeling Remove EDA Remove Exploratory Data Analysis
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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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Monitoring Your Time Series Model in Comet

Heartbeat

In this tutorial, we will go through steps on how to use Comet to monitor our time-series forecasting model. We will carry out some EDA on our dataset, and then we will log the visualizations onto the Comet experimentation website or platform. Comet has another noteworthy feature: it allows us to conduct exploratory data analysis.

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

Pickl AI

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality. ETL Tools: Apache NiFi, Talend, etc.

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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). Deploy according to various strategies.

ML 52
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Generative AI in Software Development

Mlearning.ai

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. diagram Using ChatGPT to build system diagrams — Part II Generate C4 diagrams using mermaid.js

AI 52
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Data Scientists in the Age of AI Agents and AutoML

Towards AI

Its less about just building models and more about how those models fit into scalable, business-critical systems usually in the cloud. The role of a data scientist is changing so fast that often schools cant keep up. Universities still mostly focus on things like EDA, data cleaning, and building/fine-tune models.