Introduction
Edge AI is on the adoption tipping point in the coming 24 months with analysts predicting that 50% of critical enterprise applications will reside outside of centralized locations. Organizations are rapidly pivoting to put the weight of budget and resources behind Edge AI adoption plans to capitalize on the business upside and stay ahead of competitors. This is leading to significant growth in the Edge AI space with market size predicted to expand by 24.8% CAGR to $30.56 billion in 2025 and further growth to $78.25 billion in 2029.
Contributing to the business potential of Edge AI are factors such as;
- Major hardware manufacturers such as Qualcomm, ARM, Intel, NVIDIA, are developing specialized hardware for AI inference at the edge. This hardware offers improved efficiency, lower power consumption, real-time inference, and enhanced security, even with intermittent connectivity.
- Agentic AI on the edge enables autonomous decision-making, reducing reliance on central servers. This saves bandwidth, cuts response times, and leads to faster ROI. Gartner predicts 15% Agentic AI growth by 2028.
- The expansion of 5G networks is enabling Edge AI through ultra-low latency, high bandwidth, and large scale device connectivity.
Edge AI solutions can benefit a number of industries, including retail, healthcare, manufacturing, and more. These industries are driven to adopt EdgeAI for various use cases due to a need for real-time data processing and privacy.
Challenges
While the potential of EdgeAI looks like a slam dunk there are challenges that need to be navigated by organizations seeking to adopt solutions in this space.
Edge AI Model Deployment
Edge devices naturally lack the hardware acceleration and memory capacity which can pose challenges for AI teams to deploy workloads when taking into account the variety of different hardware and software frameworks to choose from.
The process of deploying AI models can take weeks or months at the best of time for On-Premises solutions. Factor in different tools, libraries, frameworks, and environments and you can begin to see the challenges involved in manually packaging and deploying models to scale across 10’s, 100’s, 1000’s different edge devices accommodating different use cases.
Model Management and Optimization
Once workloads are deployed to the edge the job of the AI team is not over. Successful AI workload deployment requires efficient and effective model management for tracking and logging the mass scale of models across hundreds and thousands of endpoints in remote locations, potentially harsh environments, with bandwidth constraints and intermittent connectivity.
Along with this, AI teams must manage issues that come up such as models that have degraded leading to under performance that brings about the need to replace and update workloads in production. Because of this AI teams must update deployed models to many edge devices with bandwidth constraints.
Model Monitoring and Observability
The challenges that come with model monitoring and observability for edge device deployments can manifest themselves in a number of ways such as remote devices with limited processing resources, low/intermittent, high latency connections, limited or intermittent power supply to name a few. This has a tangible effect on the ability to capture observability data across edge deployments.
Wallaroo Solution
In this blog post we will address how organizations can easily and effectively overcome the challenges covered above and capitalize on the business potential of Edge AI solutions to set their business apart from the competition and maximize business impact and ROI.
Edge AI Model Deployment and Inference
The Wallaroo Universal Inference Platform is optimized to stand up ultrafast turnkey microservices on any hardware, anywhere. Built-in native integration takes full advantage of hardware investments organizations have made while also future proofing hardware investments they are going to make.
Wallaroo abstracts away every infrastructure complexity and adds automation that enables AI teams to turn models into robust, production-grade inference microservice for cloud, on-prem and edge application deployments with as little effort and complexity as possible,
Additionally in the Wallaroo platform, Edge AI Model deployment is simplified through the Auto-packaging capability by bundling all necessary artifacts into a single distributable package. Models are converted from any common or custom framework into an open format (compliant with the Open Container Initiative format, aka OCI) that Wallaroo uses to run the models. This can be done via the UI, SDK, and API depending on how model operations are being managed by an organization. Auto-packaging helps ensure consistency, portability, scalability, and reproducibility across platforms and environments. This is especially important in Edge AI use cases to ensure optimal performance on the finite hardware configurations of edge environments.
This out-of-the-box capability enables AI teams to deploy inference microservices, regardless of data science environments in which they may have been produced, with configurability and specificity as needed for target edge hardware resources such as memory, CPUs/GPUs/TPUs.
If we look at the steps to publish inference microservices to edge applications, we can see how easy and straightforward it is to accomplish with minimal code.
The following example shows how to publish a Wallaroo pipeline artifacts to the edge container registry service associated with the Wallaroo instance:
From the resulting output we can see that the pipeline status is set to “Published” along with the necessary self-contained artifacts to deploy the inference microservice at the edge using common infrastructure-as-code deployment methods such as docker and helm.
The example below illustrates running inference using Wallaroo-generated AI inference microservices an edge environment with necessary resources allocated:
curl -X POST localhost:8080/infer -H "Content-Type: application/vnd.apache.arrow.file" -H 'Accept: application/json; format=pandas-records' --data-binary @./data/image_224x224.arrow
# returns
[{"check_failures":[],"elapsed":[1067541,21209776,],"model_name":"resnet-50","model_version":"2e05e1d0-fcb3-4213-bba8-4bac13f53e8d","original_data":null,"outputs":[{"Int64":{"data":[535],"dim":[1],"v":1}},{"Float":{"data":[0.00009498586587142199,0.00009141524787992239,0.0004606838047038764,0.00007667174941161647,0.00008047101437114179,...],"dim":[1,1001],"v":1}}],"pipeline_name":"edge-cv-demo","shadow_data":{},"time":1694205578428}]
The benefit of the capability is to enable software/application developers to easily and quickly integrateAI inference microservices to power edge applications with no disruption to their application development workflows and processes, while also taking advantage of Wallaroo’s AI inference inference built-in portability to deploy performant AI applications onto diverse hardware platforms (Qualcomm, ARM, Intel, NVIDIA etc.) for large scale edge AI workload deployments regardless of use case, environment or geo location.
Wallaroo AI Control Plane
The core of the Wallaroo Edge AI solution is the Wallaroo AI Control Plane enabling centralized control to automate and scale model management, observability and optimization of models. This self-service toolkit simplifies and automates the process of deploying models to production, from packaging and containerization to scaling and serving, maximizing performance and efficiency.
Automated Observability
Model observability is essential for successful AI production. It allows data scientists to monitor deployment pipelines and ensure optimal performance. Centralized access to metrics enables proactive model and infrastructure management.
In the case of Edge AI scenarios centralized observability is even more crucial to efficient and successful AI operations due to the dispersed nature and large number of devices and models deployed. The power of edge devices enabling real time data processing to occur at the edge combined with centralized observability puts the Data Scientist firmly in control of large scale AI operations giving them the power to take swift action to ensure accurate model performance for the many remote instances in play.
Through automated observability in Wallaroo, Data Scientists can easily and quickly configure a set of validation checks and automate alerts using the Wallaroo Assays capability, enabling real-time model monitoring and data drift detection regardless of where the model is deployed. These checks and alerts can be customized to accommodate the connectivity state of the edge devices enabling the data scientists to easily generate explainability reports against all deployed models to obtain a comprehensive understanding of attributes that contributed to a certain prediction or set of predictions that inform the AI team how the models are performing, helping to ensure that they are effective in production and generating value regardless of their remote location.(Learn more about AI Edge and Multi-Cloud Automated Observability)
In certain edge AI use cases there may be instances where the edge devices do not have persistent connection to the internet. Certain scenarios such as remote telecommunications towers, oil and gas or green energy facilities, and agricultural production as a few examples of these scenarios. These situations are known as air-gapped scenarios where the edge device is disconnected from the internet for reasons such as security, unreliable connections etc. In such cases model observability is challenging for the AI team.
With the Wallaroo AI Control Plane Data Scientists can avoid flying blind and easily configure and get observability insights in these edge AI deployment scenarios where there is low or no connectivity from the edge locations.
Wallaroo’s pipeline publishing automatically packages and publishes pipelines with their models and model steps to an OCI Registry. Edge locations, added to pipeline publishes, provide authentication credentials for edge deployments to connect to the Wallaroo Ops instance. Inference results and logs from edge deployments are stored locally and persist during connection outages. Logs are transmitted back when connectivity is restored. Inference result logs are added to the associated pipeline based on the DateTime stamp of the inference. Pipeline logs store the inference location. When pipeline publishes are replaced, the associated edge locations are allocated to the new pipeline. Previous inference results and logs remain with the previous pipeline.
Looking at an example, Edge location inference log storage capacity is set with the LOCAL_INFERENCE_STORAGE and PLATEAU_PAGE_SIZE and a standard deployment with the edge observability configured for 512Mi of log storage.
Inference logs are retrieved from edge location deployments through the /logs endpoint. Learn more about Log Retrieval from Edge Locations in this tutorial.
Continuous Model Delivery
If, through the observability capabilities seen in the previous section, the Data Scientists discover models that are underperforming they will need to have a way to update and replace with a better performing model to ensure continuity of edge AI operations.
This is where the Wallaroo AI Control Plane capability extends to enabling AI teams to rapidly prototype workloads and push model updates for edge devices seamlessly with no downtime or interruption to production.
The Replace Pipeline Publish feature offers numerous benefits for AI production, including zero downtime model updates, enabling seamless integration of new models and logic without service interruption, ensuring continuous availability. It also improves model iteration, allowing data scientists to quickly replace outdated pipelines with better-performing ones, facilitating A/B testing and model retraining.
Additionally, it provides rollback capabilities to AI teams for reverting to previous versions if a new pipeline underperforms, promoting safer deployments. Furthermore, it enhances operational efficiency by eliminating the need to manually delete, recreate, and republish pipelines, supporting automation and CI/CD integration. Finally, it maintains stable inference endpoints by allowing backend logic updates while preserving the external API endpoint, preventing disruptions.
In the example here we can see how quick and easy it is with low code to update an existing published Edge AI inference microservice:
With the edge location replacement complete with the new pipeline, all the existing edges used with the original published pipeline are transferred to its replacement published pipeline to ensure continuous delivery and observability across the primary AI inference pipeline in the Wallaroo AI control plane.
Conclusion
The Wallaroo Al Universal Inference Platform allows Al teams to focus on high-value tasks providing production-grade AI inference microservices optimized for Edge AI use cases regardless of location, whether it is land, sea, or sky, ensuring organizations can access the exact same product features as cloud or on-premises solution use cases.
Wallaroo provides AI Teams intuitive self-service tools to easily and quickly control large scale end to end AI production operations on finite hardware edge devices and take full advantage of hardware innovations to deliver optimal AI performance regardless of environment and connectivity state to industrialize AI at factory scale for maximum business impact and ROI.
Next steps
Request a demo today and discover how Wallaroo can transform your AI DevOps processes, accelerate your AI initiatives, and drive business success. Visit our website to learn more about Wallaroo and the benefits of our AI DevOps platform. Don’t miss out on this opportunity to revolutionize your AI capabilities and unlock the full potential of your AI investments.
Additional Resources
