Remove Data Lake Remove Deep Learning Remove Experimentation
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. This approach is not novel.

IT 364
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250
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Make Better AI Infrastructure Decisions: Why Hybrid Cloud is a Solid Fit

CIO Business Intelligence

The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. For many nascent AI projects in the prototyping and experimentation phase, the cloud works just fine.

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Make Better Data-Driven Decisions with DataRobot AI Platform Single-Tenant SaaS on Microsoft Azure

DataRobot Blog

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. Customers can build, run, and manage applications across multiple clouds, on-premises, and at the edge, with the tools of their choice.

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Of Muffins and Machine Learning Models

Cloudera

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.