Top 4 Edge AI Trends to Look for 2024

2023 was the year AI technologies became mainstream. What once was seen in movies is now a staple in daily business operations. AI is integrated into mobile phones, shopping experiences, and daily mundane office work. From tech giants to e-commerce stores to service providers, everyone is leveraging AI to streamline operations, improve productivity, and create engaging customer experiences.

As we look ahead to 2024, it’s clear that AI’s influence will only continue to grow. One of the most exciting developments is the convergence of AI and edge computing. I recently attended the City Week 2024 event to understand its significance. After interacting with 100+ technology leaders, I understood how the powerful combination of edge and AI transforms the way we process and analyze data, bringing significant advancements to real-time analytics and decision-making at the edge. And that’s what I’m going to share with you today.

In this blog, you will learn:

How AI is Impacting the Edge Infrastructure
Top 4 Tech Predictions to Look for in 2024
How Edge Landscape Will Fare Over the Next Few Years

Let’s get started!

Top 4 Edge AI Trends to Watch for 2024

The next few years are exciting, challenging, and demanding for businesses. From innovative AI applications to advancements in edge computing, these trends are set to revolutionize technology. Let’s now analyze ground-breaking trends that will shape the future of the cloud.

1. DevEdgeOps

The significant ascent of edge computing has resulted in a paradigm shift in data processing and utilization. Edge computing, unlike traditional cloud-based methods, places computational resources closer to the data source. This shift in architecture introduces new complexities and opportunities for DevOps.

Traditional DevOps practices, though effective in cloud-centric scenarios, are not as suitable for edge computing. The “one-size-fits-all” strategy is insufficient to address the edge’s unique challenges, such as scale, connectivity, security, and device diversity. DevEdgeOps bridges this gap, enabling organizations to harness DevOps’ agility and automation while managing the complexities of edge environments. This novel approach helps enterprises in the following ways:

1. Centralized Edge Management:Real-time monitoring with dashboards, interfaces, and APIs; features for configuration, remote troubleshooting, and log analysis.
2. Edge Infrastructure Integration:Seamless connectivity with gateways, servers, and cloud services for easy deployment and scaling.
3. Edge-Aware Development Tools: Code editors, debuggers, simulators, and testing frameworks optimized for edge environments.
4. CI/CD Pipelines for Edge: Automated continuous integration and deployment with pre-configured pipelines and templates.
5. Edge Simulation and Testing:Features to simulate low-bandwidth, intermittent connectivity, and device failures for proactive issue resolution.

All these reasons are expected to drive DevEdgeOps adoption over the forthcoming years.

2. AI Interaction across Edge and Cloud

The future of AI lies in the seamless integration of edge and cloud computing in forthcoming years. AI workloads will dynamically move between the edge and the cloud, leveraging the strengths of each. The cloud will train complex AI models, while the edge will handle real-time inferencing, ensuring fast responses. Next-gen Edge platforms will support end-to-end automation, delivering comprehensive solutions across multi-cloud and edge environments. This novel approach helps enterprises in the following ways:

1. Combining the cloud’s computational power for training with the edge’s low latency for real-time tasks results in optimized performance.
2. Reduce hardware costs by offloading heavy tasks to the cloud and save bandwidth by local processing at the edge.
Easily scales workload and deploys models across new edge devices without major infrastructure changes.
3. Processes sensitive data locally at the edge, minimizing transmission risks and meeting stringent compliance requirements.
4. Stay ahead by leveraging the latest edge and cloud technologies, fostering innovation and quick deployment of AI solutions.

All these reasons are expected to drive AI Interaction across Edge and Cloud over the forthcoming years.

3. Micro AI

2024 will see the rise of Micro AI—lightweight, hyper-efficient AI models for edge devices like smartwatches, IoT sensors, drones, and home appliances. These tiny AI brains enable real-time data processing and decision-making without cloud reliance. Key innovations include better algorithms, enhanced energy efficiency, and broader applications. Micro AI helps enterprises in the following ways.

1. Cuts operational costs by reducing cloud connectivity and data transmission needs.
2. Supports immediate responses like autonomous drones and healthcare.
3. Keeps data on-device, reducing breach risks and ensuring privacy.
4. Increases system reliability by reducing dependency on internet connectivity.
5. Optimized for low power consumption, ideal for battery-operated devices
6. Enables widespread AI adoption across various devices and use cases.
7. Tailor’s AI models to specific device requirements for optimal performance.

4. AI-Based Edge-Orchestration

AI-based edge orchestration is a transformative trend for 2024, set to revolutionize edge platforms with AI-driven, policy-based deployments. These deployments feature dynamic workload migration and resource optimization algorithms, ensuring seamless workload distribution and efficient task execution. The system matches tasks with the ideal infrastructure based on location, edge topology, application availability, software/modeling versioning, dependencies between training and inference environments, and SLA criteria. It helps enterprises in the following ways.

1. Automates workload distribution to the most effective locations, enhancing overall system performance.
2. Optimizes resource allocation, reducing unnecessary computational expenses
3. Ensures tasks are executed with the right resources, minimizing latency and maximizing efficiency.
4. Consistently meets SLA standards by accounting for all critical dependencies and criteria.
5. Facilitates seamless scaling of operations by dynamically adjusting to changing workload demands.

Conclusion

After engaging with several technology professionals in the AI industry, I’ve gained valuable insights into key technologies and trends set to revolutionize the edge computing landscape in the coming years. The trends discussed in the article point to an exciting and transformative era in edge AI focused on improving efficiency and accessibility for both businesses and end-users. Stay tuned for the next edition of our AI article; I look forward to sharing more with you soon.