Parth Girish Patel
Biographic Data
| ID | 10692711 |
|---|---|
| NAME | Parth Girish Patel |
| GIVEN NAMES | Parth Girish |
| FAMILY NAME | Patel |
| SIGNATURE | PATEL P G |
| ORCID | 0009-0003-7800-9486 |
| VERIFIED | No |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
The Fundamentals of Efficient AI Workload Management
There has been a surge in the demand for efficient and scalable artificial intelligence (AI) workloads. As AI models continue to increase in complexity and size, managing and optimizing the computational resources required for training and deploying these models has emerged as a critical challenge. This chapter aims to elucidate the essential principles and practices that enable organizations to effectively harness the power of AI while maximizin…
Model Training and Inference Optimization
Model training and inference optimization is crucial for environmental sustainability in the rapidly expanding field of generative AI. As AI models grow in complexity and scale, their environmental impact becomes increasingly significant, with training and inference processes consuming substantial computational resources and energy. This chapter dives into a few ways to manage and optimize your models for deployment and inference. Specifically, t…
Cloud and Edge Computing for Generative AI
Cloud computing and edge computing both offer significant potential for enhancing the environmental sustainability of generative AI systems. By consolidating computational resources in centralized, energy-efficient data centers, cloud computing allows using renewable energy sources, advanced cooling technologies, and energy-efficient hardware, thereby minimizing environmental impact. However, the substantial energy requirements of training large …
Sustainable AI Lifecycle Management
Sustainable AI lifecycle management is an emerging discipline that addresses the environmental, social, and economic impacts of artificial intelligence throughout its lifecycle. As AI technologies become increasingly integrated into various sectors, it is imperative to adopt practices that ensure their development, deployment, and maintenance are sustainable. This chapter explores the principles and strategies for managing AI systems in a way tha…
Case Studies and Best Practices
In recent years, integrating artificial intelligence (AI) into various industries has led to significant advancements in efficiency, productivity, and sustainability. As organizations worldwide grapple with the urgent need to address environmental challenges, many have turned to AI as a powerful tool for implementing sustainable practices. This chapter explores real-world examples of sustainable AI implementations across different sectors, highli…
Optimizing Generative AI Workloads for Sustainability
Software Optimization for Generative AI
As generative artificial intelligence (AI) models continue to grow in size and complexity, the computational demands and environmental impact of training and deploying these models have become significant concerns. Large language models and other generative AI systems can consume enormous amounts of energy and contribute substantially to carbon emissions. To address these challenges, optimizing the software that powers generative AI is crucial fo…
Energy-Efficient AI Deployment and Scaling
In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), efficient deployment and scaling of applications have become crucial challenges. As AI workloads grow in complexity and resource demands, organizations are increasingly turning to containerization and virtualization strategies to optimize their infrastructure and streamline AI operations. These technologies offer powerful solutions for managing the unique…
Hardware Optimization for Generative AI
The field of generative artificial intelligence (AI) has emerged as one of the most exciting yet computationally demanding areas within AI research today. Generative models, such as large language models, possess the remarkable ability to generate human-like text, images, video, and more, leading to a wide range of beneficial applications. However, the training and operation of these complex neural network models require immense amounts of comput…
Data Management and Preprocessing for Sustainable Generative AI
Data is the lifeblood of generative AI, serving as the foundation upon which these sophisticated models are built and trained. The quality, quantity, and diversity of data directly influence the performance, capabilities, and outputs of generative AI systems. From text and images to audio and video, vast datasets are required to train models that can generate human-like content across various domains. The critical role of data extends beyond mere…
An Introduction to Generative AI
Generative artificial intelligence, also known as generative AI and GenAI, refers to a category of AI systems that use machine learning models to create new, original content rather than simply analyze existing datasets. Unlike most AI, which focuses on pattern recognition in data to optimize decision-making or predictions, generative AI can synthesize completely new data that has never been seen before by the system. At a basic level, generative…
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The Fundamentals of Efficient AI Workload Management
There has been a surge in the demand for efficient and scalable artificial intelligence (AI) workloads. As AI models continue to increase in complexity and size, managing and optimizing the computational resources required for training and deploying these models has emerged as a critical challenge. This chapter aims to elucidate the essential principles and practices that enable organizations to effectively harness the power of AI while maximizin…
Model Training and Inference Optimization
Model training and inference optimization is crucial for environmental sustainability in the rapidly expanding field of generative AI. As AI models grow in complexity and scale, their environmental impact becomes increasingly significant, with training and inference processes consuming substantial computational resources and energy. This chapter dives into a few ways to manage and optimize your models for deployment and inference. Specifically, t…
Cloud and Edge Computing for Generative AI
Cloud computing and edge computing both offer significant potential for enhancing the environmental sustainability of generative AI systems. By consolidating computational resources in centralized, energy-efficient data centers, cloud computing allows using renewable energy sources, advanced cooling technologies, and energy-efficient hardware, thereby minimizing environmental impact. However, the substantial energy requirements of training large …
Sustainable AI Lifecycle Management
Sustainable AI lifecycle management is an emerging discipline that addresses the environmental, social, and economic impacts of artificial intelligence throughout its lifecycle. As AI technologies become increasingly integrated into various sectors, it is imperative to adopt practices that ensure their development, deployment, and maintenance are sustainable. This chapter explores the principles and strategies for managing AI systems in a way tha…
Case Studies and Best Practices
In recent years, integrating artificial intelligence (AI) into various industries has led to significant advancements in efficiency, productivity, and sustainability. As organizations worldwide grapple with the urgent need to address environmental challenges, many have turned to AI as a powerful tool for implementing sustainable practices. This chapter explores real-world examples of sustainable AI implementations across different sectors, highli…
Optimizing Generative AI Workloads for Sustainability
Software Optimization for Generative AI
As generative artificial intelligence (AI) models continue to grow in size and complexity, the computational demands and environmental impact of training and deploying these models have become significant concerns. Large language models and other generative AI systems can consume enormous amounts of energy and contribute substantially to carbon emissions. To address these challenges, optimizing the software that powers generative AI is crucial fo…
Energy-Efficient AI Deployment and Scaling
In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), efficient deployment and scaling of applications have become crucial challenges. As AI workloads grow in complexity and resource demands, organizations are increasingly turning to containerization and virtualization strategies to optimize their infrastructure and streamline AI operations. These technologies offer powerful solutions for managing the unique…
Hardware Optimization for Generative AI
The field of generative artificial intelligence (AI) has emerged as one of the most exciting yet computationally demanding areas within AI research today. Generative models, such as large language models, possess the remarkable ability to generate human-like text, images, video, and more, leading to a wide range of beneficial applications. However, the training and operation of these complex neural network models require immense amounts of comput…
Data Management and Preprocessing for Sustainable Generative AI
Data is the lifeblood of generative AI, serving as the foundation upon which these sophisticated models are built and trained. The quality, quantity, and diversity of data directly influence the performance, capabilities, and outputs of generative AI systems. From text and images to audio and video, vast datasets are required to train models that can generate human-like content across various domains. The critical role of data extends beyond mere…
An Introduction to Generative AI
Generative artificial intelligence, also known as generative AI and GenAI, refers to a category of AI systems that use machine learning models to create new, original content rather than simply analyze existing datasets. Unlike most AI, which focuses on pattern recognition in data to optimize decision-making or predictions, generative AI can synthesize completely new data that has never been seen before by the system. At a basic level, generative…
Computer Science (9 works) · Artificial Intelligence (6 works) · Generative grammar (5 works) · Operating system (4 works) · Evolutionary Algorithms and Applications (3 works) · Parallel Computing and Optimization Techniques (2 works) · Advanced Data Processing Techniques (1 work) · Advanced Memory and Neural Computing (1 work) · Application lifecycle management (1 work) · Best practice (1 work)