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AI and Analytics in the Cloud

Lapland, Finland - sennarelax]
[Lapland, Finland - sennarelax]

 

- Overview

AI will make cloud computing significantly more effective. 

The AI cloud, a concept only now starting to be implemented by enterprises, combines artificial intelligence (AI) with cloud computing. Two factors are driving it: AI tools and software delivering new, increased value to cloud computing which is no more just an economical option for data storage and computation but playing a significant role in AI adoption. 

An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time. The AI cloud brings together AI hardware and software (including open source) to deliver AI software-as-a-service on hybrid cloud infrastructure, providing enterprises access to AI and enabling them to harness AI capabilities. 

A significant amount of processing power is required to run AI algorithms, making it unaffordable for many enterprises, but this deterrent is being eliminated by the recent availability of AI software-as-a-service, on the lines of software-as-a-service or infrastructure-as-a-service.

The most compelling advantages of AI cloud are the challenges it addresses. It democratises AI, making it more accessible. By lowering adoption costs and facilitating co-creation and innovation, it drives AI-powered transformation for enterprises.

The cloud is veritably becoming a force multiplier for AI, making AI-driven insights available for everyone. Besides, though cloud computing technology now is far more prevalent than the use of AI itself, we can safely assume that AI will make cloud computing significantly more effective.

 

- Why AI Cloud

The cloud and AI blend perfectly in diverse ways. AI is changing cloud service. AI might just be the technology to revolutionize cloud computing solutions. AI as a service improves the existing cloud computing solutions and engenders new paths to development. AI tools are being used to deliver more value on existing cloud computing platforms. SaaS (software-as-a-service) providers are adding AI tools into larger software suites to provide greater functionality to end-users.  

For example, the popular customer relationship management platform, Salesforce, recently added "Einstein", a tool that offers the ability to capture customer data, making it easier to track and personalize customer relationships. Einstein helps businesses identify patterns in customer interactions and provides actionable insights to improve future interactions, such as advising users on what method of contact a particular customer prefers or making recommendations to further the relationship with prospects. This new functionality has enabled customers to turn data into actionable insights they can leverage to improve sales strategy, increase customer engagement, and sell more.

 

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[AI and Cloud Computing - AI Dynamic Forces]

- The Cloud-hosted AI Platforms

Inherently AI workloads are computing and memory intensive, be it training new models or running existing models. Workloads for video, speech or large text data need huge memory and processor footprint that can be easily provisioned with cloud scaling resources in an automated way. Clients can benefit from these AI services, solutions with access to curated datasets, trained models, and an end-to-end tool stack. 

A cloud-hosted AI platform has multiple layers, the bottom-most being the infrastructure management layer, critical for ensuring that computing is cloud and hyperscaler-agnostic and scalable on-demand. 

Next comes the engineering lifecycle management layer, key in making AI vendor and technology workbench agnostic, driving standardisation and de-skilled deployment. It ensures optimised hardware use and that deployment is agnostic regardless of processor (CPU/GPU) architecture. 

The middle layer governs AI and the digital workforce responsibly while providing operational visibility. 

Then comes the API layer, allowing the larger developer community to use pre-defined base models, thereby ensuring standardisation or ‘uberising’ technology services on demand. 

The topmost layer is the experience layer that allows access to assets, enablement, and expertise, facilitating collaboration, re-use, learning, and crowd-sourcing.

 

 

 

[More to come ...]


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