Putting artificial intelligence and machine learning workloads in the cloud

Putting artificial intelligence and machine learning workloads in the cloud

Artificial Intelligence (AI) And Machine Learning (ML) Most things happen Hyped enterprise technology And the promise of efficiency and low cost and with developments like self-driving cars and autonomous quadcopter air taxis have captured the imagination of the public.

Of course, the reality is rather more plausible, with companies looking to AI to automate areas such as online product recommendations or spotting defects on production lines. Companies are using AI in vertical industries, such as financial services, retail and energy, where applications include fraud prevention and business performance analysis for loans, demand forecasting for seasonal products and crunching through large amounts of data to optimize energy grids.

All these fall short of the concept of AI as an intelligent machine along the lines of AI 2001: A Space Odyssey HAL. But it’s still a fast-growing market, driven by businesses trying to drive more value from their data, and Automate business intelligence and analytics To improve decision making.

Industry analyst firm Gartner, for example, predicts that the global market for AI software will reach US$62 billion this year, with the fastest growth coming from knowledge management. According to the firm, 48% of CIOs it surveyed have already deployed artificial intelligence and machine learning or plan to do so within the next 12 months.

Much of this growth is being driven by the development of cloud computing, as organizations can take advantage of it Low initial cost and scalability of cloud infrastructure. Gartner, for example, cites cloud computing as one of five factors driving the growth of AI and ML, as it allows organizations to “test and manage AI faster with lower complexity.”

In addition, large Public cloud providers are developing their own AI modules including image recognition, document processing and edge applications to support industrial and distribution processes.

Some of the fastest-growing applications for AI and ML are around e-commerce and advertising, as companies analyze spending patterns and use automation to make recommendations and target advertising. It takes advantage of the growing volume of business data already in the cloud, cutting the cost and complexity associated with data migration.

The cloud allows organizations to use advanced analytics and computing facilities, which are often not expensive to build in-house. This includes the use of dedicated, graphics processing units (GPUs) and extremely large storage volumes made possible by cloud storage.

“Such capabilities are beyond the reach of many organizations’ on-premium offerings such as GPU processing. This demonstrates the importance of cloud capabilities in organizations’ digital strategies,” said Lee Howells, head of AI at advisory firm PA Consulting.

Organizations are also building efficiency in their use of AI through cloud-based services. A growth area AIOpsWhere organizations use artificial intelligence to optimize their IT operations, especially in the cloud

Another hall MLOps, which Gartner calls the execution of multiple AI models, creating a “composite AI environment”. This allows organizations to build more comprehensive and functional models from smaller building blocks. These blocks can be hosted on on-premise systems, in-house or in hybrid environments.

Cloud service providers offer AI

As cloud service providers offer the building blocks of IT – compute, storage and networking – so they are developing a range of artificial intelligence and machine learning models. They are also offering AI- and ML-based services that firms, or third-party technology companies, can build into their applications

These AI offerings don’t need to be end-to-end processes, and often aren’t. Instead, they provide functionality that would be expensive or complex for a firm to provide itself. But these are also functions that can be performed without compromising the firm’s security or regulatory requirements, or that involve large-scale transfers of data.

Examples of these AI modules include image processing and image recognition, document processing and analysis, and translation.

“We operate within an ecosystem. We buy bricks from people and then we build houses and other things from those bricks. Then we deliver those rooms to individual customers,” said Mika Vainio-Matila, CEO of Digital Workforce. Robotic Process Automation (RPA) Institution. The firm uses cloud technology to enhance the delivery of automation services to its customers, including its “robots as a service,” which can run Microsoft Azure or a private cloud.

Vainio-Mattila says AI is already an important part of business automation. “Probably the most common one Intelligent document processingWhich essentially creates the meaning of unstructured documents,” he says.

“The goal is to make those documents meaningful to ‘robots,’ or automated digital agents, that work with the data in those documents. That’s where we’ve seen the most use of AI tools and technologies, and where we’ve applied the most AI ourselves.”

He sees a growing push by large public cloud companies to provide AI tools and models. Initially, it’s to third-party software providers or service providers like his company, but he expects cloud solution providers (CSPs) to offer more AI technology directly to users’ businesses.

“It’s an interesting space because the big cloud providers – clearly led by Google, but closely followed by Microsoft and Amazon and others with IBM – have implemented services around ML- and AI-based services to decipher unstructured data. This includes recognizing or classifying or translating photographs.”

These are “general-purpose” technologies designed so that others can reuse them Business applications are often very use case specific and require experts to develop them according to a company’s business needs And the focus is more on back-office operations than on applications like driverless cars

Cloud providers also offer “domain-specific” modules, according to Howells of PA Consulting. These have already evolved into financial services, manufacturing and healthcare, he says.

Indeed, the range of AI services offered in the cloud is wide and growing. “Huge [cloud] Players now have models that everyone can pick up and run,” said Tim Bowes, associate director of data engineering at consultancy Dufresne. “Two to three years ago, it was all third-party technology, but now they’re building proprietary tools.”

Azure, for example, offers Azure AI, with AI models of vision, speech, language and decision-making that users can access through AI calls. Microsoft breaks down its offerings into applied AI services, cognitive services, machine learning, and AI infrastructure.

Google offers AI infrastructure, Vertex AI, an ML platform, data science services, media translation and text to speech, to name a few. Its Cloud Inference API allows organizations to work with large datasets stored in Google’s cloud. The firm, surprisingly, offers cloud GPUs.

Amazon Web Services (AWS) also offers a wide range of AI-based services, including image recognition and video analysis, translation, conversational AI for chatbots. Natural language processing, and a suite of services aimed at developers AWS promotes its health and industry modules.

Large enterprise software and software-as-a-service (SaaS) providers also have their own AI offerings. These include Salesforce (ML and predictive analytics), Oracle (ML tools including pre-trained models, computer vision and NLP) and IBM (Watson Studio and Watson Services). IBM has even developed a dedicated set of AI-based tools to help organizations understand their environmental risks.

Specialist companies include H2O.ai, UIPath, Blue Prism and Snaplogic, although the latter three might be better described as intelligent automation or RPA companies than pure-play AI providers.

But it’s a fine line. According to SnapLogic’s Chief Technology Officer (CTO) Jeremiah Stone, enterprises are often turning to AI on an experimental basis, even where more mature technology may be a better fit.

“Probably 60% or 70% of the effort I’ve seen, at least initially, is starting to explore AI and ML as a way to solve problems that can be better solved with a better-understood approach,” he says. “But that’s forgivable because, as humans, we have a constant extreme optimism for what software and technology can do for us—if we don’t, we won’t move forward.”

Experiments with AI will bring long-term benefits, he said.

Limits and potential of cloud-based AI

AI in the cloud has other limitations. First and foremost, cloud-based services are best suited for generic data or generic processes. This allows organizations to overcome the security, privacy and regulatory hurdles involved Data sharing with third parties.

AI tools combat this by not transferring data – they reside in native business applications or databases. And security is improving in the cloud, where more businesses are willing to use it.

“Some organizations prefer to keep their most sensitive data on-prem. However, as cloud providers offer industry-leading security capabilities, the reason to do so is rapidly diminishing,” said PA Consulting’s Howells.

Nevertheless, some organizations prefer to build their own AI models and train them on their own, despite the cost. If AI is the product – and Driverless cars A prime example – would like to own the intellectual property in the business model.

But still, organizations will benefit from areas where they can use generic data and models. Weather is one example, image recognition potential is another.

Even organizations with very specific needs for their AI systems can benefit from the cloud’s extensive data resources for model training. Perhaps, they may also want to use synthetic data from cloud providers, which allow model training without the security and privacy concerns of data sharing.

And few in the industry would bet against these services being first and foremost from cloud service providers.

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