Relying on general-purpose AI models is a strategic risk that modern IT leaders can avoid by opting for dedicated cloud-based ML/AI models. Instead of building expensive server rooms, you can leverage advanced TPU and GPU units from cloud providers to create solutions perfectly tailored to your organization’s specific processes. A properly planned AI implementation in a company not only provides higher precision in niche applications but, above all, ensures full control over data security and operational costs. Discover practical strategies for machine learning in the cloud and see how market leaders turn their own algorithms into a tangible business advantage.Benefits of building and running ML/AI models in the cloud
One of the most likely
futures for cloud solutions is the use of cloud computing to train custom AI models. Solutions like ChatGPT or Gemini are useful, but for highly complex processes (e.g., production line management), ML/AI models trained on specialized datasets perform better. But why use the cloud for this purpose? There are many answers to this question, which can be divided into three categories: technological, business, and organizational. In the first case, cloud-based ML/AI models demonstrate greater efficiency and flexibility—a company choosing this path does not need to invest in building its own technological solutions, as it can use already available options like Google Cloud or Amazon Web Services. The infrastructure and software offered by providers (as well as a range of highly customizable AI tools) can serve as an excellent starting point for building custom models. Clouds provide access to advanced computing resources, such as GPU and TPU units, which are essential for training and executing complex ML/AI models, and enable easy integration with existing IT systems. This leads to the business argument—using the cloud is simply cheaper than building your own databases and server rooms from scratch. It is also worth mentioning organizational aspects—migrating to the cloud and using it to train custom ML/AI models is a step toward building a
cloud native culture. Implementing cloud computing in the workplace transforms business models and can be the first step in developing technological and organizational innovations that will improve the organization’s profitability and efficiency in the long term.
Challenges and barriers to creating custom ML/AI models in the cloud
Although creating custom AI models in the cloud can bring many benefits, there are several challenges to consider during design and implementation. The first is cost—while the cloud is scalable and provides access to advanced computing resources, usage costs can be high, especially during intensive training. The complexity of cloud processing can also be a barrier. It offers many services and tools, which is an advantage, but can also be a problem for those unfamiliar with such technologies. This multitude of tools can also be a hurdle—choosing technologies tailored to the specifics of the model can be difficult. It is also important to remember that choosing mismatched solutions can lead to performance and maintenance issues. One must not forget that creating custom ML/AI models in the cloud is complex and requires specialized knowledge and experience in machine learning, programming, and cloud services and tools, which often necessitates hiring AI specialists and developers specialized in implementing cloud solutions. Despite the advanced security mechanisms offered by providers, there is always a risk of data breaches, especially when ML/AI models process sensitive information. Another issue can be maintaining model compliance with data protection and copyright regulations,
a problem even for widely used solutions.
Examples of successful implementations of custom ML/AI models in the cloud
There are countless examples of successful custom ML/AI model implementations in the cloud, but three of them show the broad possibilities hidden in this type of solution. The first example comes from Norway, where the start-up
Strise.ai used machine learning and technologies like Google Cloud and Kubernetes to create AI for media analysis. By combining the capabilities of cloud processing and artificial intelligence, the company was not only able to expand its available database but also save computing power through automated scaling. Another example is an American logistics company for which ML/AI cloud solutions were prepared by
Pegasus One. By applying machine learning and Google OR-Tools, the logistics enterprise gained the ability to automatically determine optimal routes for vehicles and improve scheduling, which translated not only into higher efficiency but also greater satisfaction for both drivers and customers. The third example is the use of the Amazon Web Services environment by
Ignite Sales for cost optimization. Combining AWS analytical tools and cloud solutions offered by Amazon allowed Ignite Sales to identify areas where savings were possible and improved the reporting process throughout the organization.
Tips and strategies for companies planning their own ML/AI projects in the cloud
Implementing a custom ML/AI model in the cloud is one way of
optimizing costs, but it can also help improve the overall operational efficiency of an enterprise. For this to happen, it is good to follow a few best practices. The most basic strategy for AI implementation in the cloud is to set clear and measurable goals—technologies are implemented to achieve specific results, so clearly defining expected outcomes can accelerate the implementation process. It is also important to remember that the biggest challenges related to artificial intelligence in the cloud are not about technology, but about the people who implement it and who will use it. It is therefore good to build a team of machine learning specialists, developers, and data analysts, but also people who do not deal with this on a daily basis, as they may notice aspects that might be invisible or obvious to experts. Combining technical and non-technical perspectives allows for a holistic approach to the project, which translates into better results. Choosing the right cloud service provider is also of great importance—they should offer tools and services tailored to the needs of the enterprise and flexible (ideally pay-as-you-go) pricing models. Before deploying an ML/AI model at full scale, it is good to conduct a pilot implementation. This will allow you to assess the effectiveness of the model in real, yet controlled conditions. During this time, you can conduct tests to better understand how a custom AI model can help optimize business processes.
