In production environments, guidance on artificial intelligence (AI)

In production environments, guidance on artificial intelligence (AI) ...

Artificial intelligence (AI) is steadily making its way into the enterprise arena, but significant challenges remain in getting it to a place where it may be beneficial to the business model. Until this happens, the technology risks losing its focus as an economic game changer, which might hamper adoption and leave organizations without a clear path forward.

This is why AI deployment has taken center stage this year. Although everything from the lab to the production is never easy, AI can be particularly problematic considering it offers a wide spectrum of possible outcomes for every problem it is intended to solve. This means organizations must proceed both carefully and quickly so as not to fall behind the curve in an increasingly competitive environment.

Steady progress deploying AI into production

According to IDC, 31 percent of IT decision-makers believe AI has been developed, but only a third of that group considers their deployments to be at a significant stage. This is defined as a moment it begins to improve customer satisfaction, automating decision-making or streamline processes.

Being able to handle data and infrastructure at a scale that AI requires to deliver real value remains one of the biggest challenges. Even in the cloud, building and maintaining data infrastructure at this scale is no easy task. Equally difficult is properly adjusting data to weed out bias, duplication, and other factors that can skew results. While many organizations are using pre-trained, off-the-shelf AI platforms that are relatively inexpensive, they tend to be less adaptable and difficult to integrate into legacy workflows.

Scale is not just a matter of scope, but also a mix of coordination. Sumanth Vakada, the founder and CEO of Qualetics Data Machines, believes that while infrastructure and lack of dedicated resources are key obstacles to scale, so that other organizations are facing challenges, such as the siloed structures and isolated work cultures. These tend to keep critical data from reaching AI models, which leads to dissatisfactory outcomes. Only organizations have given much effort to make sure that AI is focused on

The case for on-premises AI infrastructure

While it might be tempting to utilize the cloud to provide the infrastructure for large-scale AI deployments, a recent paper by Supermicro and Nvidia is pushing back against this notion, at least in part. The companies argue that on-premises infrastructure is a better fit under certain circumstances:

  • When applications require sensitive or proprietary data
  • When infrastructure can also be leveraged for other data-heavy applications, like VDI
  • When data loads start to push cloud costs to unsustainable levels
  • When specific hardware configurations are not available in the cloud or adequate performance cannot be assured
  • When enterprise-grade support is required to supplement in-house staff and expertise

If the infrastructure itself fails to meet a reasonable price structure and physical footprint, a website deployment may be designed along the same ROI factors as any third-party solution.

In terms of scale and operational proficiency, it appears that many organizations have put the AI cart before the horse that is, and they want to reap the benefits of AI without investing in the proper means of support.

Jeff Boudier, the head of AI language developer Hugging Face, said recently that without proper support for data science teams, it becomes extremely difficult to effectively develop and share AI models, code, and datasets. This contributes to project managers'' workload as they endeavor to integrate these elements into production environments, which also increases productivity. This is because it is supposed to make work easier rather than harder.

Many organizations, in effect, are still attempting to depose AI into the pre-collaboration-version-control phase of traditional software development rather than as an opportunity to create a modern MLops environment. AI is only as effective as its weakest link, therefore if development and training are inadequate, the whole initiative might fail.

Deploying AI into real-world scenarios is probably the most crucial stage of its evolution, because this is where it will finally prove itself to be a boon or a bane to the business model. For the moment at least, there is more risk to implementing AI and failure than holding back and risk being outplayed by increasingly intelligent competitors going forward.

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