Cloud

With AI Platforms on the Cloud, Businesses Reinvent Their Processes — and Themselves

One of the biggest advantages to shifting to the cloud is access to sophisticated AI and machine learning services.

Melissa Delaney is a freelance journalist who specializes in business technology. She is a frequent contributor to the CDW family of technology magazines.

When 12-year-old Herbert Henry Dow set out to invent a chicken egg incubator, it took 40 tries before he was successful. For much of the past 125 years since he founded his namesake chemicals company, scientists at Dow have followed in his footsteps, spending months in the lab researching formulations for new products.

But what used to take months can now take seconds. Dow’s polyurethanes division, which creates custom formulations for everything from sneaker soles and mattresses to insulation and flooring, has transformed the labor-intensive research process into a digital one. The data that scientists once pored over manually is now fed into Microsoft Azure’s machine learning and artificial intelligence tools, which can instantly run through algorithms and suggest new formulations for scientists to explore.

And Dow’s machine learning and AI use hasn’t been limited to the polyurethane lab. “We are using this platform to develop and deploy analytics through Dow’s end-to-end processes, from accelerating innovation to agile supply chain planning and helping our customer service reps improve our customer experience,” says Brandon Schroeder, associate director for enterprise data platforms and business intelligence at Dow.

For instance, Dow’s digital operations center uses web-connected cameras, Azure Video Analyzer and Microsoft Power Apps to monitor facilities and provide real-time analytics to detect containment leaks in the company’s production environment and alert operators so they can take immediate action.

“Azure offers Dow the ability to quickly scale up and down, provides the right level of security controls and enables us to more quickly realize our digital aspirations,” says Schroeder.

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A Growing List of Use Cases for AI and Machine Learning

Dow is one of many businesses across industries that are using the machine learning and AI tools on public cloud platforms to reinvent processes or create new products and services. With the technologies available in the cloud, businesses large and small can experiment with machine learning and AI, and scale capacity as needed.

“The cloud offers a growing number of tools and services that make it easy for businesses to develop, test, enhance and operate machine learning systems without making big upfront investments,” says Ritu Jyoti, group vice president at IDC’s worldwide AI and automation research practice.

The technologies have come a long way in a short time. In IDC’s 2019 AI StrategiesView survey of 2,000 organizations, only 11 percent of AI initiatives were in production. By 2021, 33 percent were. There are several reasons for the uptick, but a major factor has been the shift to public cloud platforms during the pandemic, which has enabled businesses to take advantage of the machine learning and AI tools the cloud offers.

Another benefit is that cloud platforms provide an integrated development environment. On-premises data scientists often operate in silos. “Everybody is working on a separate notebook,” says Jyoti. “There’s a lack of collaboration.”

The integrated development environment was a big help to Dow during the pandemic, says Clinton Schmidt, global predictive intelligence leader at Dow. When polyurethanes lab staff were forced to work virtually, they were able to use Azure’s cloud-based predictive capability to create a virtual lab interface and continue their work, he explains.

WATCH: Learn how AI can help your organization realize its full potential.

Filtering Out the Noise With Audio Tagging

A picture may tell a thousand words, but finding the perfect sound can be daunting. If it exists, it’s probably in Splice’s catalog — the world’s largest royalty-free library of sound samples. Artists can access millions of samples through the New York-based company’s monthly subscription service.

The challenge is describing a sound to search for it. While there are common terms used to evoke visual images, sound is harder to capture with words. Audio tagging with text descriptors has long been used in searches, but it has limitations when searching for a very specific sound, explains Alejandro Koretzky, head of machine learning and audio science innovation at Splice.

Rather than limit clients to words, Splice created its Similar Sounds feature. Applying proprietary machine learning models, artists can search by sound — a precise vocal phrase or pulsing techno base, for instance — to pull up similar samples.

Splice created the feature by training machine learning models and building data pipelines with SageMaker, the machine learning platform available through Amazon Web Services. Similar Sounds uses machine learning methods to measure how close sounds are to one another, Koretzky says. He envisions using machine learning and AI in the future to further enhance music discovery and search, support decision-making and automate mastering.

“We are working on ways to remove friction from the process of discovery and creation in modern music-making,” says Koretzky. “These are multidimensional problems at the intersection of tech, workflow and user experience.”

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Investing in the Future of Data Science

Voya Financial has been using AI and machine learning to grow its three lines of business — health, wealth and investment management — explains Brian McLaughlin, the company’s vice president of enterprise data science.

In 2020, the company started using Azure Databricks and Data Factory to convert raw data, and Azure machine learning to drive Voya’s AI models.

“The cloud provides us with new and unique tools that enable us to go after really complex and challenging data science applications,” McLaughlin says, adding that the Azure machine learning suite offers power, speed, scalability and flexibility.

“It provides a much-improved environment compared with any sort of on-prem capability,” he says. Plus, it lets his team focus on building data models as opposed to managing hardware.

Voya has been applying algorithms to analyze factors such as Securities and Exchange Commission filings and market trends to help make more data-driven investment decisions.

“We’re seeing really strong results,” McLaughlin says.

Voya also uses the Azure tools within its distribution space to identify employers that would benefit most from Voya’s product offerings, as well as to better understand customer needs to help improve their retirement readiness, explains McLaughlin.

“Data is key, and we feel really good about the data that we have,” he says. “Now, it’s really about starting to build out those personalization models to drive meaningful business results and meaningful impact to our customers.”

Harry Campbell/Theispot