How To Develop Cloud With Artificial Intelligence And Machine Learning

Digital transformation (DX) is the key driver behind all activities within an organization. In addition to increasing business agility, it introduces innovations to help teams communicate, collaborate, and get work done. A closer look reveals that companies favor cloud technologies as part of the DX movement. These empower IT to provision new application servers and infrastructure whenever necessary and the most advanced solutions allow businesses to deploy high-performance IT infrastructure in minutes instead of months.

Enter AI and ML

Companies are investing time and money in developing Machine Learning (ML) to a certain stage without any human intervention. Data Science plays an integral role in building the future of both ML and Artificial Intelligence (AI). The fusion of ML and cloud computing is what we know as ‘The Intelligent Cloud’. 

On its own, the cloud delivers IT services on-demand. These typically include tools and applications such as servers, data storage, databases, and software. If we go beyond this and venture into The Intelligent Cloud, we can learn from massive amounts of information, generate predictions, and dynamically analyze scenarios. This appears to be an excellent platform to carry out tasks with greater accuracy and efficiency.

Possibilities For Business

Not long ago, when we needed extra storage for application development, we searched for data center solutions and then inserted physical disks into machine racks. This has changed into creating production ML systems. If we bring cloud computing into the equation, we can move in a competitive direction. What native techniques could not accomplish in months now takes little to no time. 

The cloud offers two core functionalities which are prerequisites for running AI system-scalable and affordable computing and storage resources. This takes us to the concept of AI and ML in cloud that enables intuitive, connected user experiences. For instance, digital assistants like Apple Siri and Amazon Alexa leverage AI technology and cloud resources to facilitate purchases, adjust a smart thermostat, etc.

Users can also take advantage of this combination to work more intelligently rather than scrolling through manual algorithms. AI workloads, in particular, are crucial because they represent greater percentages of IT infrastructure resources. Solution providers are accelerating, automating, and optimizing AI-ready workloads via pre-built compute, storage, and interconnect resources. They also monitor, manage, scale, and protect application infrastructures in which administrators deploy these elements. 

AI tools deliver more value to existing cloud computing platforms. An example is a SaaS provider who offers greater functionality by adding these tools into extensive software suites. Consider how Salesforce Einstein captures client data to simplify the process of tracking and personalizing customer relationships. This paves the way to valuable insights that Salesforce uses to improve sales strategy, enhance customer engagement, and sell more. 

AI For Cloud Computing

Using the cloud is integral for generating ML models when applying a large set of data to specific algorithms. These models are capable of learning from numerous patterns that emerge from available data. As we provide more information for a certain model, the prediction improves along with its accuracy. For example, with ML models that detect tumors in healthcare, specialists use scores of radiology reports to train the system. Any industry can customize this pattern based on its unique project requirements. The data is the required input that can appear in various forms such as raw or unstructured data. 

CSPs now offer virtual machines (VMs) with robust GPUs due to sophisticated computation methods that require a blend of GPUs and CPUs. Moreover, organizations are automating ML tasks using services like serverless computing, batch processing, and container orchestration. Even IaaS platforms assist with handling predictive analytics that utilizes ML techniques and statistical algorithms to provide a realistic assessment of future outcomes.

Interestingly, you can access services that AI systems provide even without creating a unique ML model. Examples include text analytics, speech, and machine language translation that developers can integrate into their development tasks. Users also rely on cognitive computing to train personalized data and deliver well-defined services. The cognitive capabilities of AI and ML shine where there are large volumes of data that quickly become accessible and scalable in cloud setups. Notably, before cloud solutions, the majority of AI work was isolated and expensive due to complex data, hardware, and software requirements of algorithms. Today, the cloud ecosystem enables ML efficiencies in a highly accessible manner.  


Companies run modern AI algorithms to achieve meaningful results with remarkable accuracy, precision, and speed. Even SMBs can harness the potential of AI solutions via AI-as-a-Service (AIaaS). Similar to SaaS, AIaaS offers AI technology for a monthly price and utilizes processing power in the cloud, so a digital business can deploy it anywhere. This minimizes upfront costs and one can even scale-up as their needs evolve. 

Organizations will realize real savings when they utilize data insights to offer better customer service, perform analyses without human intervention, and deploy efficient processes that improve their bottom line. The accessibility of cloud application development via the Internet eliminates expenses related to onsite hardware as well as software purchases and setup. IT departments need not deal with centers, servers, and 24/7 electricity to power and cool the servers.

Increased Productivity

AI streamlines workloads and automates repetitive tasks within IT infrastructure to boost productivity. It plays an integral role in automating key processes, and its growing analytical capabilities enable systems to run daily operations independently. Also, unlike a local storage device or hard drive that involves tasks such as setting up hardware, patching software, racking and stacking, cloud computing is Internet-based. This gives IT more time to focus on strategic functions that ensure a profitable and sustainable future for businesses.

Big Data

If you specialize in enterprise software or mobile application development, you probably know that AI, Big Data, cloud computing, and IoT take the lead in transforming brands. These technologies no longer remain sophisticated options because they have emerged as must-haves for every business. The convergence of these fields is useful in sectors especially construction and manufacturing where fast processing and productivity directly impact revenue.

Let’s explore how businesses can connect these technologies:

  • Big Data Analytics & Cloud

Organizations must be able to derive actionable insights from their stored data, and many are building Big Data analytics on top of their public cloud infrastructure. On the other hand, pure-play vendors provide cloud-subscription to enhance their market share. Banking, manufacturing, and the federal government are just some industries that capitalize on the benefits of Big Data analytics services. 

  • Cloud Infrastructure

CSPs often use SaaS to allow companies to easily process information. Generally, a console that can process specialized commands and parameters is available, but a website’s UI can also perform these tasks. Products that come with this bundle include cloud-based VMs and containers, database management systems, ML capabilities, and more. 

So, what happens is that large, network-based systems generate Big Data in either a standard or non-standard format. If the data is in a non-standard format, CSPs may utilize both AI and ML to standardize the data. From there, a cloud computing platform can harness the data and use it in innovative ways, such as searching and editing for future insights. This cloud architecture takes massive chunks of data from intensive systems and interprets them in real-time. The latest components of the SaaS model such as AI even deliver insights based on the Big Data they have gathered. With a thoroughly planned system and a nominal fee, companies especially SMBs can take advantage of this. 


At Clouve, our experts help IT professionals identify workloads to process in your AI DevOps pipelines across your computing infrastructure. We understand that AI covers a variety of projects that involve building, deploying, assessing, and refining data-driven algorithms in production applications. We fit these projects into DevOps phases: preparation, modeling, and operationalization. 

We also offer cost-efficient and competitive cloud solutions for SMBs dealing with bursty AI or ML workloads. You can experiment with ML features and scale-up as your projects go into production. You can access intelligent capabilities without requiring deep knowledge of AI, ML theory, or data science. Clouve shatters the barriers to entry for bringing all these technologies to your enterprise applications, so get in touch with us today to learn more!