IDC predicts that by 2025 there could be 41.6 billion connected IoT devices, or “things,” generating a staggering 79.4 zettabytes (ZB) of data.
Organizations are realizing the computing needed to support a 24*7 mentality generating mammoth amounts of data, often from devices out in the physical world. This greatly influences a shift in their priorities as companies look for technology that gives them the flexibility to innovate anywhere in their environment. Connected things can prompt their environment, be remotely monitored, controlled – and gradually make decisions and take valuable actions on their own. The chief use of the data generated from IoT devices could be used to trigger such alerts. The most significant technology trend moving forward for a connected world would be: the evolution of cloud strategies to include edge and hybrid investments.
The above-stated hybrid cloud journey empowers companies to handle their future hypergrowth and technology needs efficiently. Fuelling the transformation towards an intelligent edge, companies can now extend their computational AI to the edge of a network to unlock advanced business scenarios. Although, edge AI technologies often need to be architected meticulously for every solution and require an almost advanced data-scientist-level of skill and experience to develop, deploy, and operate.
The push and pull for edge AI
Edge AI & Edge Computing will aid nearly every industry—from retail and manufacturing to smart buildings and logistics. The integration of AI into an IoT implementation empowers customers to take a step beyond remote monitoring and open up further capabilities for predictive analytics, maintenance, and optimization so that you can sense, know, and act on real-time insights.
While most developers and organizations can attest to edge AI’s benefits, they often face both cost and time challenges with its end-to-end development, deployment, and management. These potential bottlenecks include training AI models, creating low power yet high-performance hardware, seamless provisioning of workloads, management and updating of devices and applications, integrating with existing applications, and helping to ensure the data and models are secured.
This is the prime reason why Microsoft introduced Azure Percept—the world's most comprehensive, easy-to-use platform with added security for creating edge AI solutions. Additionally, having the right deployment matter will make the integrations and implementations much more seamless, error-free, and less time-consuming.
Azure Percept - Solving challenging friction points
There are a few significant bottlenecks in the journey to create and deploy AI solutions for the edge at scale.
- Helping to ensure the security of the hardware and software platform used in solutions while protecting the data and AI models created by the user.
- Navigating the end-to-end AI solution creation and lifecycle management process to ensure the best performance and stability of these software components at the edge.
The principal goal of the Azure Percept platform is to simplify the process of developing, training, and deploying edge AI solutions, making it effortless for more customers to take advantage of its offerings.
In particular, the most successful edge AI deployments today require engineers to design & build devices, data scientists to additionally build and train AI models to run on those devices. An engineering and data science expertise is undoubtedly a unique set of skills held by different groups of highly trained people. But with Azure Percept, the technical bar needed to develop edge AI-based solutions, and citizen developers can build these without deep embedded engineering or data skills.
Azure Percept Key Differentiators
- End-to-end edge AI platform including hardware accelerators integrated with Azure AI and IoT services.
- Pre-built AI models and solution management to help you Kickstart your proof of concept in no time (barely minutes).
- Security measures built-in to your edge AI solution to help protect your most sensitive and high-value assets.
Services Available With Azure Percept
Azure IoT Hub, Azure IoT Edge, Azure Device Update, Azure Machine Learning, Azure Cognitive Services, Azure Custom Speech, Azure Live Video Analytics, Azure Device Provisioning Service, Azure Container Registry, and other Azure services
Leverage Cloud4C and Embrace Azure Percept
With Azure Percept, Cloud4C can help customers, and the ecosystems accelerate the development and management of edge AI solutions. It is a collaborative effort, and we’re excited to take the first step on this journey with you & Microsoft. Start working with Azure Percept & Cloud4C to accelerate your journey to deploying edge AI seamlessly.
