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A Comprehensive Guide to Hyperparameter Tuning: Exploring Advanced Methods

DZone

Hyperparameter tuning is an essential practice in optimizing the performance of machine learning models. This article provides an in-depth exploration of advanced hyperparameter tuning methods, including Population-Based Training (PBT), BOHB, ASHA, TPE, Optuna, DEHB, Meta-Gradient Descent, BOSS, and SNIPER.

Tuning 144
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

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. Auto Remediation generates recommendations by considering both performance (i.e., Multi-objective optimizations.

Tuning 217
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Machine Learning for Fraud Detection in Streaming Services

The Netflix TechBlog

In semi-supervised anomaly detection models, only a set of benign examples are required for training. Data Data Labeling For the task of anomaly detection in streaming platforms, as we have neither an already trained model nor any labeled data samples, we use structural a priori domain-specific rule-based assumptions, for data labeling.

C++ 321
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Using SLOs to become the optimization athlete with Dynatrace

Dynatrace

This post was co-authored by Jean-Louis Lormeau, Digital Performance Architect at Dynatrace. . You’ll learn how to create production SLOs, to continuously improve the performance of services, and I’ll guide you on how to become a champion of your sport by: Creating calculated metrics with the help of multidimensional analysis.

Metrics 186
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Presentation: Modern Compute Stack for Scaling Large AI/ML/LLM Workloads

InfoQ

Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how can CPUs and GPUs be utilized? By Jules Damji

Tuning 90
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How To Reduce the Costs of Database Management in Financial Services

Percona

Below, we outline some proactive steps for achieving cost efficiency and maintaining performant database environments amid a turbulent economy: 1. Tune and optimize to improve performance Even if you consider your database platform modern, simple database tuning and optimization can speed up your processes significantly.

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What is IT automation?

Dynatrace

Expect to spend time fine-tuning automation scripts as you find the right balance between automated and manual processing. AI that is based on machine learning needs to be trained. By tuning workflows, you can increase their efficiency and effectiveness. The goal of automation is to reduce IT complexity.