As AI finds its way into so many aspects of our lives, it’s even more important to understand the infrastructure that powers this valuable tool. That includes the cloud, which is a big part of how AI apps are deployed and scaled.  

Cloud computing basically refers to internet-delivered services like servers, storage, databases, networks, software, and analytics. The cloud provides computational power and ample data storage that’s needed to train, develop, and deploy cutting-edge AI models.  

If you’re looking for a way to get started with cloud technology, we recently launched a collection of free cloud courses. These courses provide you with foundational knowledge in core cloud providers including Microsoft Azure, Amazon Web Services, Google Cloud, and CompTIA Cloud Essentials+. With these cloud courses, you have the option to dive in and explore for free before committing to a cloud certification program. They’re also an excellent way to complement the in-demand AI and coding skills that you’re already learning. 

Ahead, we’ll break down the relationship between cloud computing and AI with insight from Michael Shannon, a Senior Analyst in Tech and Dev at Skillsoft, who developed the curriculum for many of our cloud courses.  

Why is the cloud relevant for the AI industry?  

The cloud has always been an integral part of advancing machine learning and AI, because it provides accessible and cost-effective computational resources to train and run AI systems, Michael says. At its core, AI involves solving math problems and continuously refining its processes. While it might seem like ChatGPT just magically churns out responses to your prompts out of thin air, it requires an enormous amount computational power to perform millions of calculations per second and store all this data.  

“Early on, cloud was an enabler for using machine learning and AI,” Michael says. If you wanted to build a deep learning engine that could replicate the speed of human thought, you needed high-end servers and computing hardware to pull it off. “It was really cost prohibitive to have high-performance computing hardware in data centers,” he says. The cloud offered a solution: Allow users to rent computing power and storage on-demand from platforms like Amazon Web Services and Microsoft Azure. This not only made advanced hardware resources accessible to a wider audience but also reduced the need for physical on-premises data centers.  

Initially, AI was a niche tool focused on business analytics and forecasting. But now, AI is bigger, better, and laced into practically everything that we do. Most recently, the generative AI boom has changed the game for businesses and people in exciting and sometimes unsettling ways. Companies are eager to seek managed services from cloud providers (more on that in the next section), who offer powerful infrastructure for machine learning and AI projects, Michael says. And individuals are leveraging AI tools like ChatGPT to streamline everything from their day-to-day work responsibilities to their home life.   

To be clear, it still takes a huge amount of computing power and energy to build, train, and use AI systems, particularly large language models, even when they run on a cloud server. A 2023 study found that generating one image with AI takes as much energy as fully charging your smartphone. Developers who rely on these AI tools need to be aware of the environmental costs and push for more efficient and sustainable solutions.  

What is AI as a service or AIaaS?  

AI as a service (AIaaS) is basically like Software as a Service (SaaS) but it focuses on providing AI capabilities over the internet. Instead of creating and maintaining AI systems in-house, companies can leverage advanced AI tools and resources from third-party providers like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Choosing the right cloud service model depends on several factors, and organizations typically utilize a combination of these models to meet their diverse needs. 

Chatbots are a common example of an AIaaS offering you might come across. Imagine that you have an ecommerce business, and you want to add a customer service chatbot that can simulate human conversations and answer real-time questions for shoppers. Rather than building a chatbot from scratch (which you can learn to do in Build Chatbots with Python), you could use an API to connect AI services like natural language processing, speech, and emotion detection. If you want to get started working with generative AI APIs, check out our free course Intro to OpenAI API. 

Which cloud providers should you learn for AI development? 

All the major cloud service providers offer their own suite of AI services, from computer vision to natural language processing and data analysis APIs. Here are just a few examples of AI offerings from different cloud service providers that you may have heard of: 

Google Cloud Platform 

• Gemini Code Assist 
• PaLM 2  

Amazon Web Services (AWS) 

• Amazon Rekognition  
• Amazon Polly 

Microsoft Azure  

• Azure OpenAI Service 
• Azure AI Vision 

CompTIA Cloud Essentials+ 

• Conversational AI 
• Recommendation Engines 

Choosing which cloud provider to learn depends on your individual goals. You can start improving your knowledge of essential cloud services and concepts for free with our new video-based cloud courses. These courses provide a high-level overview of different platforms like Azure, AWS, Google Cloud, and CompTIA Cloud Essentials+, so you can understand the value that they provide and make informed cloud service decisions.  

If your end goal is to pursue cloud certifications and a career in the field, these courses are a great way to get introduced to the breadth of cloud services out there and what they can do. You can also take these courses to better understand how these cloud service providers fit into the overall software development ecosystem. All you need is a free Codecademy membership to get started learning about the cloud.  

Start learning in-demand cloud and AI skills 

Whether you’re gearing up for a career as an AWS Developer or want a refresher on essential cloud networking principles, take a look at our new cloud hub to see all of our free courses. While you’re at it, build on your AI knowledge with courses like Intro to Large Language Models (LLMs) and Learn the Role and Impact of Generative AI and ChatGPT. Read this blog to learn more about how AI can help round out your skillset. 

The cool part about our courses and paths is that you get real-world experience working with AI while you learn these in-demand technical skills. With interactive features like AI-powered code explanation and prompt engineering built into select courses, you can witness firsthand how AI can improve and redefine your work as a developer. And when it comes time to take your cloud skills to the job market, don’t forget to use our tools like the Interview Simulator and job-readiness checker to get in the door and crush your next job interview.