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Automating Model Risk Compliance: Model Validation

DataRobot Blog

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

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A history of tech adaptation for today’s changing business needs

CIO Business Intelligence

The digitization of internal processes came in 2011, when the company decided to streamline its internal data management, quality control, project management, and communication processes through digital tools and platforms. js and React.js.

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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. This data model is also the structure of the contract that is defined between the producers and consumers of the data. Figure 2.

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Themes and Conferences per Pacoid, Episode 5

Domino Data Lab

Also, clearly there’s no “one size fits all” educational model for data science. Laura Noren, who runs the Data Science Community Newsletter , presented her NYU postdoc research at JuptyerCon 2018, comparing infrastructure models for data science in research and education. Data visualization for prediction accuracy ( credit: R2D3 ).

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Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Occam's Razor

Bonus #2: The Askers-Pukers Business Model. If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis. With that as context, you can imagine how heart-broken I was when Jane shared the following visual from a study done by Econsultancy and Lynchpin. Visual perception of information.

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Understanding the different types and kinds of Artificial Intelligence

IBM Big Data Hub

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. For example, Apple made Siri a feature of its iOS in 2011.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.