The democratization of cloud technology has resulted in fierce competition to corner the growing Artificial Intelligence market. Retailers are now structuring business models on building their revenues through AI-aided services and conquering the cloud service market. This presents a great opportunity for cloud innovation and cloud migration specialists like Kloud9 to help them adopt Artificial Intelligence Driven infrastructure (AIDI) for their retail business infrastructure.
Why AI Driven Infrastructure (AIDI)?
It’s estimated that the revenue for enterprise AI applications will increase from $358 million in 2016 to $31.2 billion by 2025, representing a compound annual growth rate (CAGR) of 64.3%. With this much at stake, Kloud9 offers them a viable solution to be a game changer for retail brands and make their cloud infrastructure intelligent.
Constrained by the cost factors involved in cloud infrastructure monitoring and maintenance, dynamic scaling of their cloud infrastructure resources during peak services and the overall process involved in maintaining their cloud infrastructure, Kloud9 offers two major areas where brands can leverage AI in retail businesses.
- Product differentiators: Our AI Driven Infrastructure (AIDI) technology solution takes over to create an enhanced, self-learning, self-monitoring and self-healing IT infrastructure. It enables continuous monitoring of Cloud infrastructure and allows for real-time recommendations for right-sizing their cloud infrastructure for peak events as well as optimize and lower their cloud billing costs.
- Cost cutting models:Migrating to the cloud is an expensive process with significant software redesign. The containerization of applications with a loosely coupled microservices architecture has created an opportunity to lower migration costs. Providing a container workload orchestration system with Kubernetes, making workloads more portable, both easing up the switch between cloud services providers, and from on-premise to their cloud retail offerings.
AIDI Success Story
Both the above aspects factored in when a leading retail brand wanted to optimize their existing cloud infrastructure to tackle the real-time scaling and performance issues seen during peak events.
While Artificial Intelligence Driven Infrastructure (AIDI) technology is available for some time, it’s only now that it’s gaining recognition. The client benefitted from adopting AIDI for their infrastructure overhaul.
- Automatic Scaling
Analyze the demand trends and predict the infrastructure requirements and plan accordingly. Match the requirements with the available infrastructure based on the workload necessities to meet peak scenarios. Thus helps in horizontally or vertically scale necessary resources in real-time to cater to current settings.
- Machine Learning Capabilities
Develop and deploy ML models based on data patterns to achieve the most optimum parameters such as availability, scalability, and storage. There is no need to define the rules, set threshold, as AI uses training to build its own model.
- Real-time Actions
Using insights based on near real-time analytics, allows the infrastructure to react or proactively act based on the single/group of infrastructure components. Helps it to act autonomously to take action for an error-free infrastructure.
- Cost Optimization
Reduce the cost of IT infrastructure by using the most optimal components. Consequently, this leads to lower IT staff, hardware, and service management cost.
- Improved Business Prospects
Due to enhanced availability and scalability, this speeds time to market, better ROI and overall brand visibility. It also lays the foundation for digital transformation with a focus on customer innovation and experience.
- Proactive System Maintenance
Identify possible infrastructure anomalies to prevent system downtime. Know about anomalies by knowing how the system behavior doesn’t look like. Identify anomalies such as intrusion detection, fraud/ fault points and save the infrastructure from abuse or going down.
Build Your AI Technology Stack
This smooth Artificial Intelligence implementation was executed by satisfying the requirements for having an AI Driven infrastructure in place that
- Supports current AI framework
The infrastructure must be designed keeping in mind both the AI applications based on AI frameworks like TensorFlow, Caffe, Theano, and Torch as well as the regular web applications and backend processes. Thus, an infrastructure should not exclusively focus on AI frameworks but design the portfolio in the interests of a developer.
- GPU optimized environments for fast computational power
AI applications require the infrastructure to simultaneously task processing in very short time cycles. For accelerating deep learning applications in particular GPU processors are employed. GPU optimized applications distribute CPU-intensive areas of an application to the GPU and let the ordinary computations be handled by the CPU. In doing so, the execution of the entire application is accelerated.
- AI integrated infrastructure services
Brands must opt for infrastructure providers who will support AI functionalities and also integrate AI as a central part of their infrastructure and service stacks. This will simplify the infrastructure setup and operations by the customer while increasing the intelligence of cloud services and applications.
- Management environment and tools that support AI frameworks and the underlying infrastructure
A current challenge is the lack of management tools needed for running AI frameworks. To provide the best balance and performance, direct interaction between the AI- Driven infrastructure and the AI frameworks is required.
AI Driven infrastructure (AIDI) encompasses every stage of the machine learning workflow to allow the algorithms to enter the production state in a stable and efficient manner. It enables data scientist, data engineers, software engineers, and DevOps teams to access and manage the computing resources to test, train and deploy AI algorithms.
AI is the future technology that can help IT organizations become more efficient and manage the increased scale of cloud-based application deployment and data.