Remove Azure Remove Cloud Computing Remove Decision Trees
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Meet the finalists of the Pushback to the Future Challenge

DrivenData Labs

Summary of approach: Our solution for Phase 1 is a gradient boosted decision tree approach with a lot of feature engineering. We used the LightGBM library for boosted decision trees because it has absolute error as a built-in objective function and it is much faster for model training than similar tree ensemble based algorithms.

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Maximizing SaaS application analytics value with AI

IBM Journey to AI blog

SaaS takes advantage of cloud computing infrastructure and economies of scale to provide clients a more streamlined approach to adopting, using and paying for software. Software as a service (SaaS) applications have become a boon for enterprises looking to maximize network agility while minimizing costs.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Familiarity with cloud computing tools supports scalable model deployment. Decision Trees These trees split data into branches based on feature values, providing clear decision rules. A solid foundation in mathematics enhances model optimisation and performance.

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Predicting the Future of Data Science

Pickl AI

A key aspect of this evolution is the increased adoption of cloud computing, which allows businesses to store and process vast amounts of data efficiently. Dive Deep into Machine Learning and AI Technologies Study core Machine Learning concepts, including algorithms like linear regression and decision trees.