Predicting Remediations for Hardware Failures in Large-Scale Datacenters

Lin, Fred; Davoli, Antonio; Alam, Imran; Kalmanje, Sukumar; Silva, Leandro; Stamford, John D.; Piazza, Jim; Sankar, Sriram

doi:10.1109/dsn-s50200.2020.00016

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Machine Learning Operations (MLOps) has emerged as a critical discipline to manage and optimize the end-to-end lifecycle of machine learning (ML) models [6]. As organizations increasingly leverage ML for decision-making and insights, MLOps plays a pivotal role in ensuring the efficiency, scalability, and reliability of ML workflows [7] . This article examines the challenges associated with implementing MLOps and presents strategies to address these issues [8].…”

Section: Machine Learning Operations (Mlops): Challenges and Strategiesmentioning

confidence: 99%

Machine Learning Operations (MLOps): Challenges and Strategies

Singla

2023

JKLST

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Machine Learning Operations (MLOps) is a critical discipline that aims to streamline and enhance the end-to-end machine learning (ML) lifecycle, encompassing development, deployment, monitoring, and maintenance. As organizations increasingly adopt machine learning models to derive actionable insights and automate decision-making, MLOps becomes indispensable for ensuring efficiency, scalability, and reliability in ML workflows. This abstract explores the challenges encountered in implementing MLOps and presents strategies to overcome these hurdles.The challenges in MLOps can be categorized into technical, organizational, and cultural aspects. Technical challenges include model versioning, reproducibility, and ensuring consistent performance across diverse environments. Organizational challenges involve collaboration between cross-functional teams, managing diverse tools and frameworks, and integrating ML workflows with existing software development processes. Cultural challenges encompass resistance to change, skill gaps, and the need for a shared understanding of ML concepts among stakeholders.To address these challenges, a multifaceted strategy is proposed. Implementing robust version control systems and containerization techniques can enhance model reproducibility and deployment consistency. Cross-functional collaboration can be fostered through the establishment of dedicated MLOps teams, emphasizing communication and knowledge sharing. The integration of MLOps into existing DevOps practices can streamline workflows and mitigate organizational silos.Furthermore, the adoption of automated testing, continuous integration, and continuous deployment practices specific to ML can bolster the reliability of ML systems. Education and upskilling programs can bridge skill gaps, while promoting a culture of continuous learning and adaptability. Open-source MLOps tools and frameworks contribute to standardization and interoperability, facilitating smoother integration into diverse ecosystems. In conclusion, the successful implementation of MLOps is crucial for organizations seeking to harness the full potential of machine learning. By addressing technical, organizational, and cultural challenges through a comprehensive strategy, businesses can establish resilient and efficient MLOps pipelines, paving the way for sustainable and scalable deployment of machine learning models.

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Section: Machine Learning Operations (Mlops): Challenges and Strategiesmentioning

confidence: 99%

Machine Learning Operations (MLOps): Challenges and Strategies

Singla

2023

JKLST

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“…Making the decision to employ a portable format requires a deep comprehension of the business and technological environment . [7] • Containerization When deploying ML models, containerization is gaining popularity as a solution to dependencies-related issues.…”

Section: Productionalization and Deploymentmentioning

confidence: 99%

Automating Model Deployment: From Training to Production

Singla,

Chavalmane

2023

JKLST

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The rapid evolution of machine learning models has led to an increased demand for efficient and streamlined processes in deploying these models from the training phase to production environments. This article explores the critical aspects of automating model deployment, focusing on the seamless transition from the development and training phase to the operational deployment in production settings.

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“…AI systems have the potential to detect, monitor, and manage heavy metal pollution, assess risk levels, and guide remediation strategies [1] . They can also contribute to energy and resource efficiency, agriculture, water management, waste management, and transportation, as well as monitor and prevent environmental damage [2] . By employing advanced algorithms, predictive modeling, and machine learning techniques, AI can help reduce climate change, improve agriculture, enhance ocean health, manage water resources, and enhance weather forecasting and disaster resiliency [3] .…”

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confidence: 99%

Interdisciplinary Insights: Integrating Artificial Intelligence with Environmental Science for Sustainable Solutions

Akter

2024

JAIGS

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This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.

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Predicting Remediations for Hardware Failures in Large-Scale Datacenters

Cited by 7 publications

References 11 publications

Machine Learning Operations (MLOps): Challenges and Strategies

Machine Learning Operations (MLOps): Challenges and Strategies

Automating Model Deployment: From Training to Production

Interdisciplinary Insights: Integrating Artificial Intelligence with Environmental Science for Sustainable Solutions

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