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Machine learning model serving architectures

Xebia

After months of crunching data, plotting distributions, and testing out various machine learning algorithms you have finally proven to your stakeholders that your model can deliver business value. For the sake of argumentation, we will assume the machine learning model is periodically trained on a finite set of historical data.

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Aquarium scores $2.6M seed to refine machine learning model data

TechCrunch

Aquarium , a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. One customer Sterblue offers a good example. investment to build intelligent machine learning labeling platform. Datasaur snags $3.9M

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List of Top 10 Machine Learning Examples in Real Life

Openxcell

But with technological progress, machines also evolved their competency to learn from experiences. This buzz about Artificial Intelligence and Machine Learning must have amused an average person. But knowingly or unknowingly, directly or indirectly, we are using Machine Learning in our real lives.

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Supervised vs. Unsupervised Machine Learning: Use Cases & Examples

Dataiku

One of the most fundamental concepts to master when getting up to speed with machine learning basics is supervised vs. unsupervised machine learning. This blog post provides a brief rundown, visuals, and a few examples of supervised and unsupervised machine learning to take your ML knowledge to the next level.

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How Banks Are Winning with AI and Automated Machine Learning

Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

Netflix Tech

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Therefore, the operational cost increases linearly with the number of failed jobs.

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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Model decay : ML models and data pipelines are notoriously “brittle.” [5]

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How Banks Are Winning with AI and Automated Machine Learning

Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data. How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models.

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Realizing the Benefits of Automated Machine Learning

While everyone is talking about machine learning and artificial intelligence (AI), how are organizations actually using this technology to derive business value? Renowned author and professor Tom Davenport conducted an in-depth study (sponsored by DataRobot) on how organizations have become AI-driven using automated machine learning.