Exploring the Battle: Edge AI vs. Distributed AI

Exploring the Battle: Edge AI vs. Distributed AI

Table of Contents

1. Introduction
2. Journey of Distributed AI
   • Cloud-Based AI
   • Edge AI
   • Distributed AI
3. Challenges in Distributed AI
   • Data Gravity
   • Heterogeneity
   • Scale
   • Resource Constraints
4. Addressing the Challenges
   • Intelligent Data Collection
   • Adaptation and Monitoring
   • Automation of Data and AI Lifecycle
   • Resource Optimization
5. Conclusion
6. FAQs

Introduction

In this article, we will explore the concept of Distributed AI and its significance in scaling data and AI applications across distributed cloud environments. Distributed AI allows businesses to Create a single pane of Glass for application lifecycle management across public cloud, on-premises, and edge environments. We will take You through the journey of how Distributed AI has evolved from cloud-based AI to edge AI and ultimately to the distributed AI we have today. Additionally, we will discuss the challenges faced in implementing Distributed AI and the capabilities that IBM has developed to address these challenges.

Journey of Distributed AI

The journey of Distributed AI starts with cloud-based AI. In this Scenario, organizations have their business operations at a local location where they make decisions. These decisions are influenced by an AI pipeline in a Core location, which could be a public cloud. The data generated during the business process is pushed to the core location, where it is used for training and inferencing of AI models. However, sending all the data to the core location can be challenging due to connectivity issues and the volume of data. This leads to the emergence of edge AI.

Edge AI decentralizes the decision-making process by leveraging distributed cloud environments and platform capabilities. The decision-making now takes place at the edge, in locations such as plants or retail stores. Application deployment and lifecycle management are handled through a container platform with data and Middleware deployed on it. The data is processed locally, and decisions are communicated back to drive downstream automation. This localization of decision-making reduces the reliance on continuous data transmission to a core location.

Challenges in Distributed AI

While Distributed AI offers many benefits, it also introduces several challenges. The first challenge is data gravity. When deploying AI applications across numerous locations, collecting and managing large amounts of data can put strain on resources and incur costs. Another challenge is heterogeneity, as each location may have unique characteristics that require tailored AI models and pipelines. The scale of deploying applications across multiple spokes and managing a wide variety of data and applications poses a complexity challenge. Lastly, resource constraints in some locations, such as limited resources in plants or retail stores, need to be considered when deploying data and AI pipelines.

Addressing the Challenges

IBM has developed capabilities to address the challenges faced in Distributed AI. Intelligent data collection allows organizations to focus on collecting only the important data and not overwhelming the system with repetitive or noisy data. Adaptation and monitoring ensure that AI pipelines and applications are optimized for each location's unique requirements. Automation is a key aspect of addressing the scale challenge, with policy-based decision making for data and AI lifecycle management. Lastly, resource optimization techniques, such as feature extraction and model compression, ensure that resource constraints are respected during pipeline execution.

Conclusion

Distributed AI offers the ability to Scale AI applications across a large number of locations and a wide variety of applications. By leveraging distributed cloud environments and platform capabilities, organizations can benefit from localized decision-making and efficient data and AI lifecycle management. IBM's capabilities in intelligent data collection, adaptation and monitoring, automation of data and AI lifecycle, and resource optimization enable the successful deployment of Distributed AI solutions.

FAQs

1. What is Distributed AI?

   Distributed AI is a paradigm of computing that allows organizations to scale their data and AI applications across distributed cloud environments. It enables localized decision-making and efficient management of the data and AI lifecycle.

2. What challenges does Distributed AI address?

   Distributed AI addresses challenges such as data gravity, heterogeneity, scale, and resource constraints. These challenges include managing large amounts of data, tailoring AI models to different locations, handling complex deployments, and optimizing resources in resource-constrained locations.

3. How does intelligent data collection help in Distributed AI?

   Intelligent data collection focuses on collecting only the important data, reducing the strain on resources and minimizing costs. This capability ensures that organizations collect and analyze Relevant data while avoiding redundant or noisy data.

4. How does IBM address the challenges of Distributed AI?

   IBM addresses the challenges of Distributed AI through capabilities such as intelligent data collection, adaptation and monitoring, automation of data and AI lifecycle, and resource optimization. These capabilities enable organizations to overcome data gravity, address heterogeneity, manage scalability, and optimize resource usage.

5. How does Distributed AI improve AI application deployment?

   Distributed AI allows organizations to deploy AI applications across a larger number of locations and a variety of applications. It enables localized decision-making, efficient data processing, and streamlined AI lifecycle management, leading to improved automation and scalability.