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Companies are looking to move from small-scale proof-of-concept AI deployments to large-scale operational AI. That’s according to Bratin Saha, vice president and general manager of Amazon AI, who spoke to VentureBeat in a recent phone interview about general trends in the AI industry.
The pandemic has spurred the adoption of AI, in part because it has led companies to digitally transform their industries. A 2021 survey commissioned by IBM find that nearly a third of the companies surveyed now use AI, with 43% saying they have accelerated existing deployments.
” We see [companies] saying: ‘How can I do [AI] a systematic engineering discipline? How to standardize this? How to introduce the right tools and procedures to make it ubiquitous? ”Saha said. “[Companies] want to go beyond the deployment of a handful of [AI] models to deploying thousands of models – in fact, [AWS has] a customer who wants to deploy a million models.
The risks of not scaling AI are substantial. Accent estimates that this could bankrupt 75% of organizations, especially if they move too slowly from experimentation to execution. In a 2019 report, the research firm found that 84% of C-level leaders believe they won’t achieve their business strategy without evolving AI, but only 16% have made progress in building an organization. powered by ‘robust AI capabilities’.
“You can use machine learning to make some really interesting demos… and they’re really compelling. But these demos… are expensive, ”Saha said. “High-profile demonstrations can capture the imagination, but they’re not repeatable – they’re expensive and don’t really deliver. [business value]. “
One of the steps businesses need to take to evolve AI is to make sure they have “good quality” data, Saha said. Another key ingredient for success is the creation of a standardized toolset, which includes software and hardware infrastructure for building and training models.
“The cloud is becoming a very important factor [in this,] because it makes the job easier [companies] standardize… [using the] same set of rules, tools and processes, ”Saha added.
Indeed, the cloud is increasingly taking into account companies’ AI scaling efforts. This is due to its potential to improve training and inference performance, while reducing costs in some cases. Even businesses with private data centers often choose to avoid increasing the hardware, networking, and data storage required to host big data and AI applications.
Hybrid cloud adoption
However, the cloud is not the ultimate solution when it comes to accelerating AI deployments. Hybrid cloud approaches have become fashionable as businesses seek to complement their on-premises infrastructure with highly scalable public clouds. For example, a hybrid AI application can leverage an on-premises database while running application code both in the on-premises private cloud and scaling to the public cloud when demand increases.
It is clear that challenges remain in scaling AI. A report from MIT Technology Review and Databricks found that only 13% of organizations are implementing their data strategy, due to issues with end-to-end lifecycle management. Other surveys cite the lack of leadership buy-in as the main reason for AI deployment failures. And still others attribute it to a lack of institutional knowledge about machine learning modeling and data science, data engineering, and business use cases.
But Saha – who has a vested interest in the success of AI services when it comes to Amazon and Amazon Web Services, it should be noted – is optimistic about the future. He points out that companies are using AI for use cases ranging from customizing their products to forecasting the supply chain of demand. They also use computer vision and many natural language processing technologies, including chatbots and intelligent document processing. “What [I] see the industrialization of machine learning coming… ”he said. “[It’s] leading to explosive growth in machine learning.
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