An "AI factory" is a concept that aims to industrialise and automate the entire process of creating and managing artificial intelligence models, in much the same way as a factory manages the manufacture of a product. Instead of a small-scale, one-off process, it creates a real AI production chain.

How it works in practice

1. Input of raw material (data): the AI factory starts upstream by collecting and centralising raw data (images, texts, figures, signals from sensors, etc.). This data is the factory's "raw material".

2. Cleaning and preparing the data: just as a factory transforms raw materials into usable materials, the AI factory cleans, sorts and labels the data. It eliminates erroneous data, standardises it (for example, setting all images to the same size, or converting text into a standard format), and structures it so that it can be easily used to train AI models.

3. Design and training of AI models: once the data is ready, it moves on to assembly; the architecture of the models (neural networks, statistical models, etc.) are chosen and trained on them. Training involves adjusting the parameters of the model so that it can recognise patterns, make predictions, classify images, translate texts, etc. The role of the factory here is to provide an automated, standardised and reproducible environment, with sufficient computing power (provided by high performance computers or HPCs) and the software tools to facilitate this work.

4. Testing, validation and quality control: just as a product leaving a production line is subject to quality control, AI models are tested on validation or test data to ensure their accuracy, robustness and reliability. The AI factory incorporates this quality control stage: if the model does not perform well enough, it is refined, retrained or adjusted.

5. Production deployment: once validated, the 'product' of the AI factory - i.e. the trained AI model - is put into service in a real environment. For example, it may be integrated into a web application, internal company software, a recommendation system or an embedded device. Automated deployment is an integral part of the "factory" approach.

6. Monitoring, maintenance and continuous improvement: the AI factory does not stop once the model has been delivered. It continuously monitors the performance of the deployed model, and if the quality of its predictions declines (new types of data, context drift), the model is retrained and improved. It's a virtuous circle: feedback from actual use feeds the 'production chain', enabling continuous correction and optimisation.

Who might need this factory?

An AI factory can be set up within any organisation (startup, SME, large company, public institution) that wants to industrialise its AI approach.

- Business departments (marketing, finance, human resources, etc.) benefit from using an AI factory to rapidly obtain prototypes and transform them into operational solutions.

- Data & AI teams (data scientists, data engineers, MLOps, developers) rely on the AI factory to industrialise their model design and deployment process.

- CIOs or IT departments use the AI factory to orchestrate the infrastructure, guarantee scalability and implement robust data governance.

- General management and senior executives find it a way of monitoring and measuring the performance of AI projects, while accelerating the organisation's digital transformation.

Why this makes a difference compared with other models

1. Automation and industrialisation

- In an AI factory, the processes of data collection, training, testing, deployment and monitoring are automated as far as possible.

- This reduces manual intervention, speeds up time to market and reduces the risk of human error.

2. End-to-end lifecycle management

- Instead of simply developing an AI model on a static dataset, the AI factory includes continuous updating, monitoring and retraining stages.

- It therefore incorporates MLOps (machine learning operations) practices, which ensure the reproducibility and traceability of experiments and model versions.

3. Scalability and re-use

- Thanks to its "factory" organisation, the AI factory makes it possible to reuse components or modules (cleaning, label generation, distributed training, etc.) for different projects.

- It is designed to scale up quickly, which is crucial in a context where the size of AI models and the volume of data are growing exponentially.

4. Better cross-team collaboration

- Data science, devops, infrastructure, security and business teams work on a common platform, with shared standards and workflows.

- Roles, tools and responsibilities are clearly defined, promoting efficiency and avoiding bottlenecks.

5. Increased quality and reliability

- Tests and validations are carried out systematically (quality control) at every stage. Models are evaluated, compared and validated on pre-production environments before going into production.

- Monitoring and alert mechanisms are built in to detect any drift or drop in performance, ensuring continuous quality.

6. Shorter iteration times

- Industrialisation facilitates rapid loop experimentation. New model architectures, new hyperparameters and new data sources can be tested, without having to rebuild everything by hand each time.

- This accelerates innovation and the continuous improvement of AI performance.

7. Visibility and traceability

- The AI factory records every stage (code version, data sets used, test results, metrics, etc.). Models can therefore be audited or explained more easily, which is essential for regulatory compliance (e.g. GDPR, AI ethics).

Challenges that may arise

Dependence on data quality: even the best infrastructure cannot compensate for the absence or poor quality of data. Data cleansing, enrichment and governance remain demanding and time-consuming issues.

Specialised resources and skills: setting up an AI factory requires a variety of profiles (data scientists, data engineers, MLOps, devops, cloud architects, etc.), which are often difficult to recruit or retain. The salary cost of these profiles and the associated training can be high.

Infrastructure costs and technological complexity: big data platforms, intensive computing services and automated deployment tools (CI/CD, MLOps) generate significant costs (licences, hosting, maintenance). The multiplication of microservices and environments (development, test, production) makes management and supervision more complex.

Organisational complexity: the AI factory involves the collaboration of multiple teams (business, data, IT, etc.), and it is sometimes difficult to align everyone around the same objectives and processes. The change in internal culture (data-driven approach, agility) is not always spontaneous and can generate resistance.

Risk of "proof of concept" projects with no follow-up: despite the organisation and industrialisation, some AI projects do not get beyond the experimentation phase, resulting in a dispersal of resources and a form of "technical debt" or "project debt".

Security and regulatory compliance: handling large quantities of data, some of it sensitive (personal data, strategic information), requires compliance with strict rules (GDPR, local laws, etc.). Cyber-security requirements increase the need for regular checks, monitoring and audits.

Scalability and ongoing maintenance of models: once in production, models need to be regularly updated and re-evaluated (data drift, changes in business needs). Industrialisation does not guarantee perfect maintenance and monitoring: a long-term organisation is needed to monitor performance, adjust and even redevelop models over time.

Read the original French-language version of this report /