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Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

Regardless of your industry, whether it’s an enterprise insurance company, pharmaceuticals organization, or financial services provider, it could benefit you to gather your own data to predict future events. From a predictive analytics standpoint, you can be surer of its utility. Deep Learning, Machine Learning, and Automation.

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How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

This enables employees to see data details like definitions and formulas, lineage and ownership information, as well as important data quality notifications, from certification status to events, like if a data source refresh failed and the information isn’t up to date. Data modeling. Data migration .

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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

This enables employees to see data details like definitions and formulas, lineage and ownership information, as well as important data quality notifications, from certification status to events, like if a data source refresh failed and the information isn’t up to date. Data modeling. Data migration .

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Unlocking Tabular Data’s Hidden Potential

ODSC - Open Data Science

Although tabular data are less commonly required to be labeled, his other points apply, as tabular data, more often than not, contains errors, is messy, and is restricted by volume. One might say that tabular data modeling is the original data-centric AI!

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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.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?