BUSINESS IMPACT

Jan 06, 2021

Cloudy with a Chance of AI: 6 Benefits to Modernizing your AI with the Cloud

Tom Weinandy Posted by Tom Weinandy

In the past decade, we have seen artificial intelligence mature from a business novelty to a business necessity - and it's still growing. According to Statista, the global AI market is expected to reach $126 billion by 2025. At the same time, more and more companies are moving their data analytics to the cloud. Here I explain why AI and cloud computing are a match made in the heavens.

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Benefits

The benefits of cloud-based AI come down to six core features:

1. Training models where the data is.
Having your data and compute together on the cloud eliminates the burden of moving one to the other. This allows for rapid and low-cost experimentation to find the best model for your business use case.
 
2. Spin up, spin down compute.
Why buy a supercomputer when you can just rent one? Scalable compute allows for strategic flexibility where you have access to virtually unlimited computational power to build AI models while only paying for what you use.
 
3. An army of computers.
The only thing better than having a supercomputer is having many virtual machines with the same capacity. By training your machine learning algorithms on the cloud, you have access to parallelized compute that can dramatically decrease build time.

4. PaaS on the cloud.
The best cloud-based services bring together security, governance, and the ability for data scientists to collaborate in real time on the same models and code. Platform-as-a-Service (PaaS) offerings alleviate most of the need to manage the underlying architecture, making the setup and management easier. Businesses that successfully migrate to the cloud benefit from having a single source of their data, as well as a single source of analytics.

5. SaaS on the cloud.
Most cloud platforms also provide Software-as-a-Service (SaaS) with programs and operating systems that cover data analytics from storage, to insights, to applications. This widens the skills bottleneck at an organization by augmenting the performance of system architects, data engineers, and data scientists.

6. Unlimited data storage.
The once-hyped term “big data” has become so ubiquitous that many individuals now just refer to it as “data.” Companies can unlock the virtually unlimited storage capacity of cloud services to use their data as a competitive advantage - and more data leads to better models.

 

These features and benefits are visible in the two primary types of cloud-based AI.

Cloud Platforms

First are cloud platforms like Microsoft’s Azure ML that covers the whole machine learning lifecycle from training models in notebooks or GUIs to deployment. It even includes the powerful AutoML feature that automatically searches dozens of possible AI models to find the best fit to your data.Machine Learning Service Workspaces-1

Pre-Built AI Services

The second flavor of cloud-based AI is in services, such as Microsoft’s Cognitive Services. These are pre-built AI-models in areas like natural language processing, computer vision, and knowledge mining. Cognitive Services allow for state-of-the-art analytics through simple API calls that free up your data scientists to focus on the core problems of the organization. A business can also develop their own custom models on the cloud that can be made available to anyone from anywhere.

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Cloud-based services have democratized AI and allowed organizations to fully leverage one of their most valuable assets: their data. Modern data analytics in the cloud may be a forecast right now, but soon enough, it will become inevitable.

Think Big, Start Small

Watching this mind-blowing tech evolve, and now implementing and managing solutions driven by it, the thing that continually stands out for our team at BlueGranite is the potential some of these systems have to change the world.

Wondering how it can potentially change your organization? Contact us today to discover how we can put your data to work for you.

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Tom Weinandy

About The Author

Tom Weinandy

Dr. Tom Weinandy is a Data Scientist and Business Economist specializing in data analytics and machine learning for industrial settings. His background experience is in nonprofits and education. Tom holds a bachelor’s in Social Entrepreneurship from John Carroll University, an M.B.A. from Wheeling Jesuit University, and a Ph.D. in Applied Economics from Western Michigan University with his dissertation entitled "Applied Microeconomics and Business Intelligence in the Digital Age.”

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