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From Data Collection to Model Deployment: 6 Stages of a Data Science Project

KDnuggets

Here are 6 stages of a novel Data Science Project; From Data Collection to Model in Production, backed by research and examples.

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How to Design Experiments for Data Collection

KDnuggets

Several factors must be taken into consideration when designing experiments for data collection.

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Streaming Edge Data Collection and Global Data Distribution

Cloudera

With the rapid increase of cloud services where data needs to be delivered (data lakes, lakehouses, cloud warehouses, cloud streaming systems, cloud business processes, etc.), controlling distribution while also allowing the freedom and flexibility to deliver the data to different services is more critical than ever. .

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Closing The Loop On Event Data Collection With Iteratively

Data Engineering Podcast

If you are struggling with inconsistent implementations of event data collection, lack of clarity on what attributes are needed, and how it is being used then this is definitely a conversation worth following.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.

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Empowering Data Teams with Snowplow for First-Party Digital Event Data Collection

databricks

With more and more customer interactions moving into the digital domain, it's increasingly important that organizations develop insights into online customer behaviors.

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Top 6 Microsoft HDFS Interview Questions

Analytics Vidhya

A distributed file system runs on commodity hardware and manages massive data collections. It is a fully managed cloud-based environment for analyzing and processing enormous volumes of data. Introduction Microsoft Azure HDInsight(or Microsoft HDFS) is a cloud-based Hadoop Distributed File System version.

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