Let’s Talk About AI, Edge, and Cloud Computing
Organizations across the world and industries will sooner or later have to migrate their AI and analytics workload to the cloud. Hence, it is not a question of IF, rather a matter of WHEN and HOW. It is the pathway to reach digital maturity and many organizations have already begun to experiment with edge and cloud computing to achieve reduced TCO, flexibility, and agility.
Edge and cloud computing has had a positive impact on AI and analytics. Some of these benefits include:
Proximity to the data source needed to draft model and to front-line applications that use analytics-driven insights to arrive at real-time decisions
It is well suited for the CI/CD/CM techniques, thus bringing repeatability and automation required to scale analytics as required. At the same time, it helps in delivering a faster time-to-value.
- This system is a perfect fit with organizations’ move towards usage-based consumption and subscription models
- Edge computing provides real-time analytics capabilities with the hassle of investing in expensive hardware and software. It also supports the need for data science professionals with code-free approaches.
However, it is still very early to judge the impact of edge and cloud computing on AI and analytics, as many early adopters are still in the experimentation phase. Hence, to realize full benefits organizations will need to incorporate edge and cloud computing into a fully operational production environment.
AI, Edge, and Cloud Computing: The Road Ahead
Organizations would benefit a lot if they embrace the experimentation phase since it has immense potential to drive the creativity of data science professionals. It will also help organizations to identify and prioritize use cases. At the same time, it is also important for organizations to figure out an appropriate strategy for cloud analytics with a crisp agenda in mind. This is important because the decisions made in the experimentation phase would not necessarily be a great fit when it boils down to scaling and governing analytics in production.
Organizations need to pay attention to the following aspects:
- Data gravity: Moving data in the world of cloud computing is expensive. Organizations need to check if they have a proper data structure in the cloud to train, rank and monitor.
- Multi-cloud strategy: Organizations need to ensure that their technical and architectural choices are not bound to a particular cloud vendor. They need to ensure ease of data migration from one cloud to another. Organizations also need to be able to govern and manage data and analytical assets across platforms.
- Technical debt: Most organizations introduce a lot of new tools and components across cloud environments, thus aggravating the complexity of the analytics ecosystem. This causes additional problems for organizations, wherein they have to find appropriate professionals to manage such complex environments.
In summary, edge and cloud computing in coordination with AI and analytics, is a key enabler for digital transformation, permitting companies to bring about innovation in an agile manner. However, organizations need to clearly define a strategy that is parallel to the experimentation of new techniques and tools.