Opinions expressed by Digital Journal contributors are their own.
The coming together of cloud computing, machine learning (ML), and artificial intelligence (AI) has been garnering a lot of attention in the tech community around the world. This much-needed convergence has already shown promises to revolutionize industries. However, ensuring flawless integration between cloud and edge ecosystems still remains to be a challenge.
Ravi Kumar Vankayalapati, a technology leader specializing in edge computing, cloud reliability, ML, and AI, has recently co-authored a research paper sharing practical insights on taking distributed AI to new heights by creating a synergy between edge and cloud computing. This paper also introduces an evolutionary framework for IT innovation and real-time processing of AI.
Understanding the importance of edge-cloud Synergy
Processing data in real-time has now become the foremost priority for organizations looking to accelerate their digital transformation initiatives. It is true that the traditional models of cloud computing are quite powerful. However, often times, these models have limitations related to data privacy, bandwidth, and latency. In their research, Vankayalapati and his fellow researchers have made a valiant effort to address these problem areas by integrating cloud infrastructure and edge computing. Their objective was to explore the possibilities of building AI ecosystems where data processing takes place closer to sources.
The combination of edge and cloud computing can be a gamechanger for addressing the rapidly changing business needs of today’s finance, telecommunications, healthcare, and many other sectors. When AI- driven applications are processed closer to mobile networks, industrial sensors, IoT devices, or any other data sources, organizations require significantly less time to generate insights from these data sources. This is the foundation of building resilient and highly responsive IT systems that can make data-driven decisions in real-time.
With edge-cloud synergy, the need for sensitive data transfer across networks is reduced significantly, which can greatly enhance compliance and security. As data is processed locally before its transmission to cloud, their exposure to cyber criminals is minimized. Also, organizations can analyze data and initiate prompt actions at the edge. This can help reduce cost, optimize operations, and explore new business avenues.
Framework for making the most of AI
In his elaborate research paper, Vankayalapati has recommended using a layered framework to ensure that computational resources are optimally allocated across cloud and edge environments. Discussed below are some critical aspects of this framework.
- Distribution of workload: Computation tasks are allocated dynamically between cloud and edge layers with the help of AI algorithms. As a result, resources are utilized optimally. The framework also ensures edge computing handles tasks such as remote medical diagnostics, smart surveillance video analytics, and other latency-sensitive activities. On the other hand, the cloud performs tasks such as model training and long-term storage of data.
- Data Processing: By performing data-sensitive operations like predictive analytics and anomaly identification at the edge, the reliance of organizations on cloud-based data centers can be reduced to a great extent. For example, by deploying AI models at the edge for industrial automation, organizations can avoid expensive downtimes by detecting machinery faults instantly.
- Optimized AI Models: While the cloud ensures model updates through continuous learning, the optimized versions of these models are executed by edge devices for real-time deductions. With this hybrid approach, AI models can be improved continuously to deliver superior performance.
- Security and Compliance: Regulatory and security compliance enhances significantly when sensitive data is processed locally before insights are transmitted to the cloud. This model can help financial institutions reduce risks of fraud through real-time analysis of transactions.
- Scalability: This extraordinary framework can be applied across many different industries, including telecommunication, autonomous systems, finance, healthcare, and many more. From managing traffic optimally in a smart city to hospitals performing remote surgeries in real-time, this framework is critical to more efficient and effective implementation of AI.
“In addition to reducing operational costs and minimizing latency, this innovative model can help unlock the true potential of AI like never before,” Vankayalapati affirms. “This framework can transform the way AI-driven technologies are used and managed by enterprises, by transitioning to a computing system that is more efficient and distributed.”
Technology leader dedicated to digital transformation
With over 14 years of top-level IT experience, Ravi Kumar Vankayalapati has developed, tested, and implemented robust solutions across complex IT environments. Some highlights of his eventful professional journey includes extensive ServiceNow expertise, operational frameworks, order management data models, and integration of AI, ML, and Cloud Security into diverse IT ecosystems to achieve scalability and innovation. He is also actively involved in leading and mentoring teams and emerging professionals, implementing strategic IT initiatives, and contributing to organizational success.
Final thoughts
At a time when industries around the world are showing keen interest in embracing the new wave of digital transformation, the study by Vankayalapati provides a blueprint for redefining the efficiency of AI-powered solutions through edge-cloud synergy.
“This vision for improved distributed AI will provide a clear strategic advantage to organizations that are foresighted. The insights gained from this study can not only help reshape IT infrastructure, but can also set new standards for transforming businesses using AI,” Vankayalapati concludes.
