Artificial intelligence (AI) and Machine Learning (ML) are currently important topics, especially for data collection and analysis. Another important topic is cloud computing, which allows us to access products and services without the need to own a physical machine.
Most importantly, all these topics can be combined into the same project. Continue this reading to understand the power of cloud computing for your AI and ML projects.
How can cloud services boost ML and AI projects?
Basically, the same work developed in a machine can be built entirely on the cloud, meaning there is only a change in the development environment that delivers more flexibility, among other advantages. Check all cloud computing advantages in this article.
When developers are already qualified to work with ML and AI software, this change can happen quite easily and effortlessly. Some initial adaptation might be required at first, even though it will eventually result in a highly intuitive interface. Regardless, professionals working in these areas must prepare for this new work environment, whether through academic/technical courses or by studying relevant documents adapted for each project.
It is worth mentioning that the cloud environment is not complex. As mentioned, its intuitive interface facilitates the learning, plus previously acquired skills in Machine Learning and Artificial intelligence speeds even more this adaptation process.
Cloud services categories
There are basically three categories of cloud services:
First category:
The first category are services developed from Artificial Intelligence yet not combined with other tools. Examples are APIs, computer vision (technology dedicated to obtaining information through images) and audio transcription.
Second category:
In this category are services developed with Machine Learning and Artificial Intelligence models, intuitively combined with other tools. The goal is to achieve a final result that shares similarities with off-cloud existing products. They will present a flexible structure and are extremely important for governance.
Third category:
The third and last category involves auxiliary services, such as storage and database. Cloud storage is valuable as it keeps an enormous amount data and files, that common server cannot handle locally. These services have a flexible and highly available structure.
Advantages of Machine Learning and Artificial Intelligence in the cloud
Cost efficiency and scalability
It will result in cost reduction considering there is no need to invest in physical equipment or software installing licenses. Services are hired according to company demands and are highly scalable.
Seamless interaction with other services or tools
In some cases, ML and AI tools in the cloud might generate codes that allow a seamless interaction with other cloud services.
Governance
Cloud environment is conductive for managers to care for governance, model’s history, and explanation (their relevance to achieve the desired goals, for example) and ethics.
Increased focus on important tasks
One of the best advantages of cloud computing is allowing the organization to have more focus and dedication to economically important activities, by automating repetitive and bureaucratic tasks that are, generally, less strategical.
Faster response time
Cloud environment is projected to offer quick response. After the necessary initial configuration, an algorithm can provide automatic answers and data analysis.
If you are looking to develop Machine Learning or Artificial Intelligence projects in the cloud, talk to one of our consultants and learn about the most efficient project management model for your business.