As a concept, artificial intelligence is very old. My first job out of college almost 40 years ago was as an AI systems developer using Lisp. Many of the concepts from back then are still in use today. However, it's about a thousand times less expensive now to build, deploy, and operate AI systems for any number of business purposes. Cloud computing revolutionized AI and machine learning, not because the hyperscalers invented it but because they made it affordable.

Artificial Intelligence (AI) is steadily making its way into all industries. From healthcare to retail, and consumer to enterprise applications, businesses are starting to unlock all the power and benefits AI and machine learning have to offer. One only needs to look at its growth: the AI services market is expected to grow 17.4% year-over-year in 2021, with revenues reaching $37.9 billion by 2024, thanks to a CAGR of 18.4%, according to Statista. Even with the pandemic-laden IT budget setbacks of 2020, AI is here to stay. While the past year has been largely about survival and prioritizing mission-critical IT initiatives, innovation and optimization are making their way back into the fold as work begins to stabilize.

Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.

Cloud computing has made a lot of technology more accessible, and artificial intelligence and its underlying technologies are no exception. If you want more organizations to be able to use your technology, then make it possible for them to use it on one of the big public cloud providers -- Microsoft Azure, Google Cloud Platform, and Amazon Web Services (AWS). Indeed, many organizations are now using the AI services that are available and have been built on those public cloud platforms -- AWS Rekognition, for instance. In an effort to broaden the distribution of its flagship artificial intelligence technology, IBM this week announced that it is making IBM Watson portable across all these public cloud services. The company unveiled the strategy this week at the IBM Think 2019 event in San Francisco.

Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Knowledge Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.

Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Knowledge Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.

The first wave of cloud computing is attributed to platforms. Google App Engine, Engine Yard, Heroku, Azure delivered Platform as a Service (PaaS) to developers. The next big thing in the cloud was Infrastructure as a Service where customers could provision virtual machines and storage all by themselves. The third wave of cloud was centered around data. From relational databases to big data to graph databases, cloud providers offered data platform services covering a wide range of offerings.

Deep learning has emerged as a cutting-edge tool for training computers to automatically perform activities like identifying stop signs, detecting a person's emotional state, and spotting fraud. However, the level of technological complexity inherent in deep learning is quite daunting. So how can one get started? Forrester analyst Mike Gualtieri provides a surprising answer. "The easiest way to possibly do deep learning," Gualtieri said during a session at Teradata's recent user conference, "is not to do it."

An unlikely partnership between two tech heavyweights symbolizes how cloud vendors prioritize machine learning and deep learning for the future of their platforms. Amazon Web Services (AWS) and Microsoft Azure are the two most popular public cloud providers, with the latter trying to encroach on the former's sizable market lead. But in a surprise move, the pair has put aside their rivalry to create Gluon, an open source deep learning library intended to automate certain processes and make machine learning more approachable to developers. Both of these companies, as well Google, IBM and others, see huge potential for machine learning in the cloud and deep learning applications built on their respective platforms. But these techniques are predominantly confined to the likes of data scientists, because typical developers lack the skills to build and train the models that underlie these applications.

Artificial intelligence (AI) is a hot topic these days. We're told everyone can use it. We all have our own ideas of what AI can do– some very accurate, some a bit, well, paranoid. If we set aside the AI fiction depicted in the Terminator movies, what is the reality of AI, and how does it apply to today's enterprises? is a hot topic these days. We're told everyone can use it.