AI and Analytics in the Cloud

[Lapland, Finland - sennarelax]

- Overview

On a larger scale, AI capabilities are coming into play in commercial cloud computing environments to make organizations more efficient, strategic, and insight-driven. Cloud computing offers businesses greater flexibility, agility and cost savings by hosting data and applications in the cloud. AI capabilities are now combined with cloud computing to help businesses manage data, find information patterns and insights, deliver customer experiences, and optimize workflows.

AI-driven initiatives provide strategic input for decision-making and are backed by the flexibility, agility and scale of the cloud to support such intelligence at scale. The cloud significantly increases the reach of AI, starting with the user enterprise itself and then the larger market. In fact, AI and the cloud will complement each other, with the cloud helping AI to blossom into its true potential.

This speed will only depend on the AI ​​expertise that businesses can leverage in their workplace activities, as the cloud is already here and everywhere. Enterprises’ investments in AI use will be multiplied by the cloud; this makes the AI ​​cloud very attractive.

- The AI Cloud

AI will make cloud computing significantly more effective. 

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.

AI can be divided into two broad categories:

• Narrow AI: designed to perform specific tasks
• General AI: Attempts to perform intellectual tasks typically performed by humans

Additionally, cloud computing is a modern computing method that allows users like you to access and use resources such as storage, processing power, and software over the Internet without the need to install local hardware or software.

The three main service models of cloud computing include:

• Infrastructure as a Service (IaaS)
• Platform as a Service (PaaS)
• Software as a Service (SaaS)

When we combine AI with cloud computing, AI algorithms can take advantage of the huge computing power and storage space in the 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.  

[AI and Cloud Computing - AI Dynamic Forces]

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.

The popular customer relationship management platform, Salesforce, added "Einstein", a tool that offers the ability to capture customer data, making it easier to track and personalize customer relationships. 

Salesforce Einstein is a set of AI technologies that use machine learning, natural language processing, and other AI techniques to analyze data, uncover insights, and automate tasks. It can be used across the entire Salesforce platform, including its industry-specific clouds for marketing, sales, commerce, and customer service. 

Here are some ways Salesforce Einstein can help businesses:

• Einstein GPT: Combines public and private AI models with CRM data to allow users to ask natural-language prompts directly within Salesforce CRM. The result is AI-generated content that is continuously adapted to changing customer information and needs.
• Einstein Copilot: A conversational AI assistant for CRM that can help sales, service, commerce, and marketing teams get work done faster and focus on customers. Einstein AI Copilot can answer natural language questions and give data-driven answers securely obtained from the organization.
• Salesforce Commerce: Provides product recommendations, tailored product sorting, and meaningful search results to help improve customer experience during shopping.

Other ways Salesforce Einstein can help businesses include:

• Discovering insights that bring new clarity about a company's customers
• Predicting outcomes so users can make decisions with confidence
• Recommending the best actions to make the most out of every engagement
• Automating routine tasks so users can focus on customer success
• Generating tailored content, such as emails, knowledge articles, and code

Salesforce Einstein is the first comprehensive AI for CRM. It's an integrated set of AI technologies that makes the Customer Success Platform smarter and brings AI to trailblazers everywhere.

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.

[More to come ...]