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How to build a data project with step-by-step instructions

Start Data Engineering

Understand input datasets available 3.1.2. Define what the output dataset will look like 3.1.3. Define checks to ensure the output dataset is usable 3.2. Introduction 2. Parts of data engineering 3.1. Requirements 3.1.1. Define SLAs so stakeholders know what to expect 3.1.4. Identify what tool to use to process data 3.3.

Project 240
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Building a PubMed Dataset

Towards Data Science

Step-by-Step Instructions for Constructing a Dataset of PubMed-Listed Publications on Cardiovascular Disease Research Continue reading on Towards Data Science »

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Building ETL Pipeline with Snowpark

Cloudyard

In this blog, well explore Building an ETL Pipeline with Snowpark by simulating a scenario where commerce data flows through distinct data layersRAW, SILVER, and GOLDEN.These tables form the foundation for insightful analytics and robust business intelligence. Built clean, enriched datasets in the SILVER layer.

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Getting Started with Amazon SageMaker Ground Truth

Analytics Vidhya

Building an accurate machine learning and AI model requires a high-quality dataset. Introduction In this era of Generative Al, data generation is at its peak.

Datasets 243
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Introducing Cloudera Fine Tuning Studio for Training, Evaluating, and Deploying LLMs with Cloudera AI

Cloudera

Fine Tuning Studio enables users to track the location of all datasets, models, and model adapters for training and evaluation. Build and test training and inference prompts. This means that data scientists can build and develop their own training scripts while still using Fine Tuning Studio’s compute and organizational capabilities.

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Best Practices for Building ETLs for ML

KDnuggets

This article talks about several best practices for writing ETLs for building training datasets. It delves into several software engineering techniques and patterns applied to ML.

Building 146
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How to get datasets for Machine Learning?

Knowledge Hut

Datasets are the repository of information that is required to solve a particular type of problem. Datasets play a crucial role and are at the heart of all Machine Learning models. Datasets are often related to a particular type of problem and machine learning models can be built to solve those problems by learning from the data.