In industry, digital transformation is increasingly based on the use of data from production equipment and processes. Artificial Intelligence (AI) and Machine Learning (ML) bring new solutions, as they enable us to optimize the performances of industrial equipment, anticipate malfunctions and improve product quality. However, it’s not always easy to adopt these technologies and bring projects to life in-house.
Various difficulties observed in AI adoption
The relevance of data
When it comes to monitoring equipment, it’s not always clear whether the data collected will be relevant for generating models, particularly for predicting breakdowns. For the most part, companies have begun to instrument installations after the fact depending on the project and service, using different technologies. When a data science project begins, it is often necessary to gather the various data, prepare them and explore their content to identify whether they will be relevant to the development of predictive models. These tedious tasks are often carried out by data scientists at the start of the project.
Data Scientists, a non-specialized cross-functional function
Firstly, Data Science resources are new teams within companies. When they do exist, these new professions are often cross-functional and respond to the company’s many and varied projects, making them a highly coveted and sought-after resource. Used for business, production and other projects, Data Scientists bring specific skills in coding, processing and manipulating data, but rarely have the field experience, functional expertise or specific business knowledge required to validate the models they generate. This is why they need to call on the knowledge of a domain expert for their project.
Domain experts, the project bottleneck
On the other hand, domain experts often lack the coding and data science skills to access these technologies easily. Occupying key functions within the company, these experts are necessary to the operational running of the business, and often don’t have the time to dedicate to innovation projects. Nevertheless, their knowledge is key to the development of Machine Learning projects, making them highly sought-after resources.
That’s why the no code becomes a major asset.
No code: a feature for domain experts
No-code is defined as a characteristic of simplified tools that enable the creation of a digital solution or content without any knowledge of computer languages. Thanks to intuitive no-code interfaces and good AI explainability, it becomes possible for domain experts to exploit Machine Learning models without advanced programming skills.
Indeed, by removing the technological barrier, field experts gain the ability to take full advantages of data without requiring the intervention of data scientists. In this way, they retain their autonomy and accelerate decision-making. This democratization opens up new prospects for industry, where speed and autonomy in data analysis are becoming key competitive factors.
DiagFit: dedicated software for domain experts
Our DiagFit software has been designed from the ground up with domain experts in mind, thanks to :
- An intuitive, code-free interface: users can configure and operate the various functions without writing a single line of code. All they have to do is import their industrial time series and follow the methodology proposed by the software to explore the heart of the series.
- Task automation: DiagFit offers a range of automated functions, from data preparation to the generation of anomaly detection models. All these tasks can be tackled by domain experts with no data science expertise.
- Saving time and autonomy: not only can domain experts immediately work on their data and obtain results in just a few clicks, but the software has also been designed for very rapid operations and calculation times.
At Amiral Technologies, we are convinced that the future of industry lies in the autonomy of domain experts, and the democratization of Data Science knowledge. No code is a strategic ally in this transformation.
Discover now how to transform your industrial data into concrete action!
