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Open-Sourcing AvroTensorDataset: A Performant TensorFlow Dataset For Processing Avro Data

LinkedIn Engineering

However, we found that many of our workloads were bottlenecked by reading multiple terabytes of input data. To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data. Avro serializes or deserializes data based on data types provided in the schema.

Datasets 102
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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. Also called data storage areas , they help users to understand the essential insights about the information they represent. Datasets play a crucial role and are at the heart of all Machine Learning models.

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30+ Free Datasets for Your Data Science Projects in 2023

Knowledge Hut

As Data scientists, our focus is on both the quality and quantity of data which can improve the model results. With different sources of data, we can leverage the information to drive good business understanding. Your data should possess the maximum available information to perform meaningful analysis.

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

Start Data Engineering

Parts of data engineering 3.1. 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. Identify what tool to use to process data 3.3. Data flow architecture 3. Introduction 2. Requirements 3.1.1.

Project 240
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20 Best Datasets for Data Visualization

Knowledge Hut

The choice of datasets is crucial for creating impactful visualizations. Demographic data, such as census data and population growth, help uncover patterns and trends in population dynamics. Economic data, including GDP and employment rates, identify economic patterns and business opportunities. Census Bureau The U.S.

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Last Mile Data Processing with Ray

Pinterest Engineering

Behind the scenes, hundreds of ML engineers iteratively improve a wide range of recommendation engines that power Pinterest, processing petabytes of data and training thousands of models using hundreds of GPUs. transformers) became standardized, ML engineers started to show a growing appetite to iterate on datasets.

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Cloud authentication and data processing jobs

Waitingforcode

Setting a data processing layer up has several phases. You need to write the job, define the infrastructure, CI/CD pipeline, integrate with the data orchestration layer, and finally, ensure the job can access the relevant datasets. Let's see!