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Companies are looking to move from small-scale, proof-of-concept AI deployments to operating AI at a massive scale. That’s according to Bratin Saha, VP and general manager at Amazon AI, who spoke with VentureBeat in a recent phone interview about general trends in the AI industry.
The pandemic supercharged the adoption of AI, in part because it caused companies to digitally transform their lines of business. A 2021 survey commissioned by IBM found that almost one-third of business surveyed are now using AI, with 43% reporting that they accelerated existing rollouts.
“We’re seeing [companies] saying, ‘How do I make [AI] a systematic engineering discipline? How do we standardize this? How do we introduce the right tools and procedures to make this pervasive?,” Saha said. “[Companies] want to go beyond deploying a handful of [AI] models to deploying thousands of models — in fact, [AWS has] one customer that wants to deploy a million models.”
Scaling up AI
The risks of failing to scale AI are substantial. Accenture estimates that it could put 75% of organizations out of business, particularly if they execute the shift from experimentation to execution too slowly. In a 2019 report, the research firm found that 84% of C-level executives believe they won’t achieve their business strategy without scaling AI, yet only 16% had made inroads creating an organization powered by “robust AI capabilities.”
“You can use machine learning to do some really cool demos … and they’re really compelling. But those demos … are expensive,” Saha said. “The high-profile … demos can capture the imagination, but they they’re not repeatable — they come at high costs and they’re not really providing [business value].”
One of the steps enterprises must take in scaling AI is ensuring they have “good-quality” data, Saha said. Another key ingredient for success is creating a standardized set of tools, which includes software and hardware infrastructure for building and training models.
“The cloud becomes a very important factor [in this,] because it makes it easy for [companies] to standardize … [using the] same set of rules and tools and processes,” Saha added.
Indeed, the cloud is increasingly factoring into enterprise AI scaling efforts. This is due to its potential to improve training and inferencing performance, while also lowering costs in some cases. Even companies that have private datacenters often opt to avoid ramping up the hardware, networking, and data storage required to host big data and AI applications.
Hybrid cloud adoption
The cloud isn’t the end-all be-all when it comes to ramping up deployments of AI, however. Hybrid cloud approaches have come into vogue as companies look to complement their homegrown infrastructure with highly scalable public clouds. For example, a hybrid AI app might tap an on-premises database while running app code both in the on-premises private cloud and scaling to the public cloud when demand increases.
It’s clear that challenges remain in scaling AI. An MIT Technology Review and Databricks report found that just 13% of organizations are delivering on their data strategy, owing to issues around managing the end-to-end lifecycle. Other surveys cite a lack of executive buy-in as a top reason for AI deployment failures. And yet 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 as they relate 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 from personalizing their products to forecasting demand supply chain. They’re also using computer vision and a lot of natural language processing technologies, including chatbots and intelligent document processing. “What [I] see coming down the road is the industrialization of machine learning…” he said. “[It’s] leading to explosive growth in machine learning.”
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