Leveraging Cloud-Based AI/ML Services to Elevate your Business

By 2030, PricewaterhouseCoopers estimates artificial intelligence will contribute $15.7 trillion to the global economy. According to this research, 45% of total economic gains by 2030 will come from product enhancements that stimulate consumer demand, as AI will drive greater product variety with increased personalization, attractiveness and affordability. 

Since using artificial intelligence (AI) and machine learning (ML) will provide businesses with innovative solutions to a wide variety of problems, what is stopping companies from harnessing the power of AI? Organizations’ main challenge revolves around the resources needed to incorporate AI/ML into their existing workflows and processes. Cloud computing AI/ML services can help to overcome this obstacle.

Read more: AIOps vs MLOps: What’s the Difference?

AI – technology for special assignments

In machine learning, data patterns, correlations, and trends are identified by computer models through experience, as machine learning models can provide additional insights into data. Meanwhile, artificial intelligence refers to the use of machine learning to automate tasks that typically require human-like intelligence. People can perform such tasks, but the right AI can do them faster and more efficiently. 

A machine-learning model is constructed by applying a large dataset to an algorithm. The created model learns from various patterns derived from available data – the more data delivered to the model, the better the result. Maximizing the power of AI/ML models requires extensive computing power furnished by cloud services providers. Using cloud-based AI/ML services enables organizations to access powerful machine learning algorithms and tools without needing specialized hardware or in-house expertise, making it more accessible and cost-effective for companies of all sizes to adopt ML-driven solutions.

Generative AI Services

Cloud computing services driven by AI/ML provide a scalable and flexible platform for machine learning. With cloud-based services, enterprises can scale up machine-learning efforts without investing in additional hardware or infrastructure. Additionally, given the availability of off-the-shelf AI/ML tools, the cost of producing an effective solution is lower compared to the amount of time it takes a human to complete a job. It’s a significant benefit for organizations that must process large amounts of data or handle high traffic volumes. The benefits do not end there, as cloud-based AI/ML services easily integrate with other cloud-based tools and create seamless and efficient workflows.   

Generative AI use cases in 2025  

Content creation

Generative AI is now a cornerstone of marketing and media, automating written content production, including articles, social media updates, and email campaigns tailored to a specific brand’s voice and audience. Beyond text, it is used to create realistic images, video clips, and music for advertisements and entertainment, dramatically cutting down production timelines and costs that were once prohibitive for smaller companies. 

Product design and engineering

Generative design is transforming the landscape of physical products. Engineers can input specific constraints like material type, weight, and stress tolerance, and the AI generates thousands of optimized design options for a variety of items, from car parts to consumer goods. This approach accelerates innovation and produces highly efficient designs that are often non-intuitive and may not occur to human designers. 

Software development

Generative AI acts as a collaborative partner for developers. AI-powered tools autocomplete complex code, suggest optimal algorithms, and even translate natural language prompts into functional code blocks. This “co-pilot” approach significantly boosts productivity, reduces human error, and automates routine tasks like writing unit tests and documentation, freeing up developers to focus on more strategic problem-solving, eventually leading to vibe coding movement.  

User experience (UX) personalization

This is where Generative AI truly shines in creating adaptive experiences. It is used to personalize user interfaces in real-time, dynamically altering the layout, content, and features of a website or application based on an individual’s browsing behavior. Furthermore, advanced generative chatbots and virtual assistants can now hold nuanced, context-aware conversations, providing personalized customer support that feels remarkably human. 

AI/ML in digital transformation – key industries 

By 2025, AI and ML have become the central engines driving digital transformation, moving beyond niche applications to become core components of business strategy across major industries. These technologies are essential for creating intelligent, automated, highly efficient operations that adapt to rapid market changes. 

The impact of AI and ML is particularly profound in several key sectors, where they unlock new levels of efficiency and customer value. 

Fintech

AI and ML are revolutionizing financial services by automating complex processes and personalizing customer interactions. This includes AI-driven algorithms for real-time fraud detection and automated credit scoring, significantly reducing risk and operational overhead. Furthermore, ML-powered robo-advisors make personalized investment strategies accessible to a broader audience, tailoring recommendations based on individual financial goals and risk tolerance. 

Healthcare 

In healthcare, these technologies are pivotal for improving diagnostics and optimizing costs. ML models can analyze medical images like MRIs and X-rays to detect diseases with a speed and accuracy that can surpass human capabilities. Simultaneously, AI optimizes hospital workflows by predicting patient admission rates and managing bed allocation, leading to significant cost savings and better resource management. 

Manufacturing  

The “smart factory” has become a reality thanks to AI and ML. Predictive maintenance, where ML algorithms analyze sensor data from machinery to forecast failures, is a prime example, preventing costly downtime and extending equipment life. AI-powered robotics and computer vision automate quality control and complex assembly tasks on the assembly line, increasing production speed and precision while reducing errors. 

Retail

For retailers, AI and ML are the keys to hyper-personalization and supply chain efficiency. ML algorithms analyze customer data to power sophisticated recommendation engines and implement dynamic pricing strategies that maximize revenue. Behind the scenes, AI optimizes inventory management and demand forecasting, ensuring that products are available when and where customers want them, which minimizes waste and lost sales. 

Real-case scenarios of cloud-supported AI/ML

 Machine learning models that leverage cloud services can help many industries, even business endeavors that may seem unrelated to AI/ML. One example is the scrap metal industry, where you can use AI to identify scrap quantities from satellite images – a solution that provides significantly better results than legacy systems. Use-case scenarios do not end there. 

As more and more industries lack sufficient specialists, machine learning models can replace specific processes previously carried out by humans. One real case example of such technology is performing an automated verification of the correctness of a telecommunications installation using image analysis. No technician is required on-site, as the customer can upload all the photos needed for the system to run an analysis.

Read also: What is machine learning model management?

The more advanced the project gets, the more machine learning engineering it needs. Cloud services support working with models for anomaly detection, natural language processing (NLP), cognitive services, computer vision and AutoML mechanisms. There’s no denying that building the necessary pipelines to prepare and process data is more efficient with the cloud.  

A use case of cloud-supported machine learning engineering worth mentioning is an advanced mechanism built for recognizing and categorizing objects in images that a company can apply in various industries:  

• In the retail industry, to analyze the number of products of a specific brand on the shelves and the amount of traffic in stores.  
• In the industrial and mining industries, to verify the volume of traffic in the factories, to help detect anomalies in the operation of machinery, maintain health and safety rules and verify work attire. 
• In the ecommerce industry, to support the creation of bots that communicate with users or to vastly expand website traffic analysis.  

What is MLOps? Why is it crucial for AI?  

To manage a machine learning model correctly, it is worth referring to the application lifecycle management, because both scenarios work on similar principles. The key is to design and implement mechanisms to gather data, train ML models accurately, and then deploy them to dev, test, stage and production environments. Optimized models must be monitored based on performance, adhere to the highest security standards, and run and trained at scale in a distributed model.  

Considering the scope of such an endeavor, organizations with in-house data science services departments that develop machine learning projects will eventually need support using the cloud at some stage of work. Machine Learning Operations (MLOps) practices can improve the quality and consistency of machine learning solutions, by combining the power of ML, data engineering, and software engineering to improve the development and deployment of ML models and streamline the continuous delivery of high-functioning models into production. 

Microsoft describes MLOps as a set of principles and practices, similar to DevOps, that increase the efficiency of workflows, with the main goals of faster experimentation and development of models, as well as quality assurance and end-to-end lineage tracking. The MLOps service involves support in projects and preparation with the potential implementation of appropriate standards tailored to a project’s needs. 

As MLOps evolves, it integrates deeply with DevOps practices, particularly CI/CD pipelines. This integration means the entire ML workflow is automated, from data validation and model training to testing and deployment. Any change to the code or the underlying data can trigger this pipeline, ensuring that models are consistently reproducible and reliable. 

This framework is powerfully enhanced by scalable deployments in cloud environments. Cloud platforms like AWS, Google Cloud, and Azure provide the elastic infrastructure and specialized tools necessary for MLOps at scale. They offer managed services for model training, containerization for consistent deployment using tools like Kubernetes, and advanced monitoring systems. 

Challenges regarding integrating AI and ML in companies 

Despite significant advancements, successfully integrating AI and ML into organizational workflows in 2025 presents substantial challenges. These hurdles often go beyond the algorithms, touching on core issues of resources, infrastructure, and existing systems. 

One of the most significant barriers is the persistent talent gap. The demand for experienced data scientists, ML engineers, and MLOps specialists far outstrips the available supply, making hiring and retaining the necessary expertise incredibly competitive and expensive. Beyond hiring, there is the challenge of upskilling existing staff. Many companies struggle to bridge the knowledge gap between their traditional IT and business teams and the specialized data science units, hindering effective collaboration.

Infrastructure presents another major hurdle. AI and ML models, intensive learning, require immense computational power for training, often necessitating significant investment in specialized hardware like GPUs. While cloud computing provides a scalable alternative, the associated costs can quickly become prohibitive without careful management. Furthermore, many organizations lack foundational data infrastructure. AI initiatives often stall due to poor data quality, siloed information, and the absence of robust data pipelines to feed and train the models effectively.

Finally, a major roadblock is integrating modern AI platforms with legacy IT systems. These older systems were not designed for the data volume or real-time processing demands of AI, making integration technically complex, costly, and risky. Ensuring that deployed models are scalable, secure, and maintainable within this hybrid environment remains a critical and often underestimated challenge. 

Read more: Machine learning in marketing

Generative AI in 2026: Focus on marketing

In 2026, AI and ML have become the central nervous system of modern marketing, enabling a strategic shift from broad targeting to hyper-individualized customer engagement. The key trends revolve around using AI to understand and predict customer behavior with unprecedented accuracy. 

Hyper-personalization at scale

Instead of just recommending products, generative AI will create unique real-time marketing content for each individual. This includes dynamically generating personalized email copy, social media ads, and even video content that reflects a user’s specific browsing history and predicted interests. The goal is to make every brand interaction feel like a one-on-one conversation. 

Predictive user data analysis

The focus has shifted from analyzing past behavior to predicting future actions. ML models are now sophisticated enough to accurately forecast a customer’s lifetime value (CLV) and identify individuals at high risk of churn, allowing for proactive retention campaigns. Furthermore, AI analyzes unstructured data from customer service chats and social media comments to gauge public sentiment and identify emerging market trends before they become mainstream. 

Intelligent marketing automation

Marketing automation in 2026 is far more than scheduled email campaigns. AI now orchestrates entire customer journeys across multiple channels in real-time, deciding the best message, channel, and time to engage with each user. A crucial development is AI-powered budget allocation, where algorithms continuously analyze campaign performance and automatically shift marketing spend between channels to maximize return on investment (ROI) without human intervention. 

AI/ML services will lead the way for businesses

Sudi Bhattacharya and Ashwin Patil, managing directors at Deloitte Consulting LLP, expressed it well in their blog post, “It’s easy to see how the cloud helps fuel AI/ML to drive insights and innovation. However, it takes planning and insight to get there. Cloud-fueled AI/ML takes vision, a solid foundation, and education coupled with a governance discipline”. Experts help to train, set up and run ML systems on the cloud, but the innovative solution will not completely replace human ingenuity. The practical and technical limitations of AI/ML do not allow it to understand every single situation correctly and respond in the best conceivable manner. The key to success is, and will continue to be, the right collaboration between humans and the potential offered by AI/ML services, allowing you to leverage the sheer power of cloud services cost-efficiently to gain a competitive .edge. 

If you want to know how cloud computing AI/ML platforms can elevate your products and services, contact us to take advantage of artificial intelligence’s great potential.

Read more about Cloud Business Inteligence

FAQ 

What is an AI/ML service?

An AI/ML service is a cloud-based platform that provides businesses with access to artificial intelligence and machine learning capabilities without building the underlying infrastructure. Offered by providers like Google Cloud, AWS, and Azure, these services include pre-trained models for tasks like image recognition and natural language processing, and tools to build, train, and deploy custom models. They manage the complex hardware and software, allowing companies to easily integrate powerful AI capabilities into their applications on a pay-as-you-go basis. 

The four primary types of Artificial Intelligence are categorized by their functionality. 

What are the four types of AI tools?

Reactive machines represent the most basic type, operating solely on present data without the ability to form memories or learn from the past. A classic example is IBM’s Deep Blue, the chess computer that defeated a world champion. Limited memory AI can use recent past experiences to inform cunt decisions, as seen in self-driving cars that monitor the speed and direction of nearby vehicles. The next two types are still theoretical. Theory of mind AI would understand human emotions and thoughts. Finally, self-awareness AI is a hypothetical future stage where machines possess consciousness. 

Is ChatGPT an AI or an ML system? 

ChatGPT is both. The relationship can be seen as a hierarchy: Machine Learning (ML) is a core subfield of the broader Artificial Intelligence (AI) category. ChatGPT is powered by a Large Language Model (LLM), a technology built using deep learning, an advanced ML type. Because its core functionality is based on learning from data, it is a powerful ML system, making it a prominent example of an AI system.