Last updated on August 12th, 2022

Charles Babbage was one of the pre-eminent polymaths of the 19th century. He was a mathematician, philosopher, inventor, and mechanical engineer though we primarily associate his name with the Difference Engine and the Analytical Engine.

Babbage’s analytical mind led to this observation by him about trees:

Every shower that falls, every change of temperature that occurs, and every wind that blows, leaves on the vegetable world the traces of its passage; slight, indeed, and imperceptible, perhaps, to us, but not the less permanently recorded in the depths of those woody fabrics.

The lives of trees have remained the same but in two centuries, human technology has progressed to include supercomputers, cloud computing, neural networks and the very beginnings of quantum computing.

Perhaps no other technology is playing as pervasive a role in the ongoing digital transformation of the lives of organizations as cloud technology.

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Cloud Promises

McKinsey projects that there is a trillion dollars in value “up for grabs in 2030” if companies learn to harness the cloud to the maximum extent possible.

There are many “cloud cost-optimization levers and value-oriented business use cases,” as McKinsey puts it.

McKinsey notes that the value drivers include IT cost optimization, risk reduction, digitization of core operations, innovation-driven growth, accelerated product development, and hyper-scalability.

Certain sectors are poised to “generate the most value as measured by EBITDA impact in 2030,” per McKinsey.

Cloud, McKinsey notes, “has been long heralded as a catalyst for innovation and digital transformation, thanks to its ability to increase development speed and provide near-limitless scale.”

One of the particularly beneficial use cases of the public cloud’s Infrastructure as a Service (IaaS) offering is cloud AI. AI-powered BI (Business Intelligence) is one more beneficial public cloud IaaS offering.

Also Read: What is Public Cloud

How AI/ML Services on The Cloud Help Organizations

A diverse array of AI services has now reached maturity through decades of research and are now ready to be built upon and deployed by developers and data scientists in organizations.

A variety of AI models and neural networks are now available through APIs so that organizations can create their own machine learning models with open-source frameworks such as TensorFlow.

It’s interesting to speculate how Galileo, Copernicus, and Mr. Babbage would react to the present age of AI where televisions and smartphones are commonplace — even mundane — technologies and we have robots failing in a funny way but also robots that dance perfectly in tune with the music.

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Modern Business Intelligence (BI) has become quite sophisticated with the use of agile systems to analyze data more quickly.

With AI in the picture, AI-powered BI can help deliver transformative solutions to businesses in a range of industries including financial services, healthcare, retail, and manufacturing.

AI can help deliver insights that help financial firms prevent fraud. ML models can help detect anomalies.

Running ML algorithms on customer data can offer insights about customer behavior which in turn can help improve customer service, and help financial institutions come up with a more personalized product offering or portfolio management option.

Cloud AI Helps Businesses Across Several Verticals

Use of AI/ML capabilities such as analysis of unstructured data and natural language processing can improve core banking operations.

In manufacturing, AI can help improve quality control and help with proactive maintenance so that you have minimal or no downtime for your mission-critical systems.

One of the reasons why aircraft engines send data to their manufacturers via satellites is to help with predictive maintenance — it’s another matter that those data and signals of ham radio operators crisscrossing the world might help solve mysteries such as the disappearance of Malaysia Airlines Flight MH380.

And in retail, AI-powered virtual agents can help with the smooth handling of customer service requests and help improve customer experiences when companies deploy ML algorithms to detect and analyze customer trends and behavior.

In healthcare, the use of AI-powered tools can lead to faster drug discovery and speedier diagnoses. ML-based analytical tools can accelerate data mining and knowledge mining.

These are obviously not the only industries that benefit from AI/ML. Automotive, insurance, telecom and other sectors have lots to gain by using AI/ML as well.

Leveraging Cloud AI Infrastructure for Developing ML Models

Imagine the business insights that would become available when all your CRM and ERP data is available on the cloud and you can get up-to-date info about product sales trends and customer behavior on customizable dashboards.

All this becomes super-easy when data is available round-the-clock on cloud databases and cloud storage with multiple backups in multiple separate physical locations.

All these gains are available in a scalable, reliable, and redundant manner just by transferring data to the cloud.

Machine learning models, however, require orders of magnitude more data to train the ML algorithms and make the algorithms as capable as possible.

Google made their datasets publicly available after using images of cats to train and build its own algorithm that specialized in identifying cat faces.

ML models need the data to identify the patterns in them. More data increases the accuracy of predictions.

Deep learning and neural networks involve complex computations which are most suited for computing environments that are configured with GPUs.

Cloud AI service providers such as Ace Cloud Hosting offer clusters of GPU-infused VMs on a pay-as-you-use model.

Other public cloud compute services such as containerization and serverless computing are also available for parallelizing and also automating ML tasks.

Companies can thus train the ML models on their unique datasets and then deploy the ML models to VMs and containers.

The elastic infrastructure made available by IaaS providers enables high-throughput computing that can handle predictive analytics in the real world.

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Leveraging Cloud AI Services to Build Better Applications

When developers want to add powerful image recognition or speech recognition capabilities to their apps, the best way to go about it is to use the various relevant APIs for the cognitive computing tasks.

These reinforcement learning based neural networks are available as APIs being provided by public cloud providers who offer cloud AI services.

It becomes easy to integrate these AI capabilities with applications via an API call.

Customers can also use their own data to develop specialized services which removes the constraints of generic data and generic use cases.

One of the use cases for these cognitive computing APIs is helping developers integrate voice and text bots into their applications via bot services provided by public cloud service providers.

It becomes easy — via these bot services or APIs — for web and mobile developers to add virtual assistants to their apps.

Also Read: Object Storage Vs Block Storage

Leveraging AI Tools — Wizards, IDE, Data Preparation Tools for ETL, Frameworks — For Maximizing Gains from ML

Several of these AI tools make things easy for data scientists and developers. It has become a complex affair, for example, to set up, install, and configure required data science environments.

Frameworks such as TensorFlow and others are provided by cloud service providers as ready-to-use VM templates. This is useful for training complex neural networks and ML models on GPU-powered VMs.

Ensuring the quality of data is important to gain the maximum efficiency from ML models — data preparation tools that perform the extract, transform, and load (ETL) are provided by public cloud vendors.

Summary — How AI/ML APIs Help Organizations

Use of APIs can greatly accelerate software development. The benefits of AI/ML APIs include:

  • Making it easy for data scientists to choose the best algorithms, datasets, and more.
  • Helping organizations create sophisticated analytics solutions faster.
  • Find a workaround for the lack of skilled persons in AI.
  • Helping choose the best ML model.
  • Helping commercialize solutions.

Also read: Hybrid Cloud vs Multi Cloud

Conclusion

Clearly, there is potential in utilizing public cloud functionalities such as cloud AI and cloud AI services within the broader domain of public cloud. Organizations that harness this potential will tend to thrive and grow rather than shrink .

After all, organizations — as much as humans and trees — also have lifecycles. Organizations can take steps to live long and grow large or they can atrophy and die.

The dynamic ones who understand the urgencies of our thriving and competitive business age will be the ones who flourish for long and leave enduring marks on our civilization.

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