Remove Data Preparation Remove Datasets Remove Raw Data
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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. They need to: Consolidate raw data from orders, customers, and products.

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Spotter: Your AI Analyst

ThoughtSpot

Level 2: Understanding your dataset To find connected insights in your business data, you need to first understand what data is contained in the dataset. This is often a challenge for business users who arent familiar with the source data. In this example, were asking, What is our customer lifetime value by state?

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Simplifying BI pipelines with Snowflake dynamic tables

ThoughtSpot

When created, Snowflake materializes query results into a persistent table structure that refreshes whenever underlying data changes. These tables provide a centralized location to host both your raw data and transformed datasets optimized for AI-powered analytics with ThoughtSpot.

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Audio Analysis With Machine Learning: Building AI-Fueled Sound Detection App

AltexSoft

Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. But first, let’s go over the basics: What is the audio analysis, and what makes audio data so challenging to deal with. Labeling of audio data in Audacity.

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Tableau Prep Builder: Streamline Your Data Preparation Process

Edureka

Tableau Prep is a fast and efficient data preparation and integration solution (Extract, Transform, Load process) for preparing data for analysis in other Tableau applications, such as Tableau Desktop. simultaneously making raw data efficient to form insights. Choose your dataset and click Open.

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Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

There are two main steps for preparing data for the machine to understand. Any ML project starts with data preparation. You can’t simply feed the system your whole dataset of emails and expect it to understand what you want from it. What should it be like and how to prepare a great one?

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Enabling The Full ML Lifecycle For Scaling AI Use Cases

Cloudera

While it’s important to have the in-house data science expertise and the ML experts on-hand to build and test models, the reality is that the actual data science work — and the machine learning models themselves — are only one part of the broader enterprise machine learning puzzle. Laurence Goasduff, Gartner.