Data, Ops & Cloud in business: from innovation to production
b<>com transforms your data and machine learning projects into robust, operational solutions that can be deployed at scale.
b<>com supports you at every stage: from data processing to the industrial-scale deployment of machine learning models, drawing on its combined expertise in DevOps, MLOps and enterprise cloud solutions. b<>com guarantees industrial-grade deliverables: reproducible, robust and ready for direct deployment in its clients’ live environments. For innovation departments, b<>com is the partner that bridges the gap between research and operations.
The 4 areas of expertise: Data, Ops & Cloud
| Areas | Symptoms | Objectives | Key skills |
| Data processing | Our models produce inconsistent results depending on the data sources. We spend more time cleaning the data than analysing it. | Ensuring data reliability prior to the learning process. | Multi-source data collection, pre-processing, data cleansing, anomaly detection, annotation. |
| DevOps | The code works on the developer’s machine but not in production. We are unable to reproduce exactly what was delivered six months ago. | To ensure the robustness and reproducibility of deliverables. | CI/CD, versioning, multi-OS builds, member deployment. |
| MLOps | Our model worked well at launch but is gradually deteriorating. Our data changes frequently and your model is deteriorating in production. | To scale up and ensure the long-term viability of ML/AI models in production. | Scale conversion, monitoring, drift control, re-training. |
| Cloud | Our solution only runs on a specific environment, which we cannot always guarantee. We are looking to deploy on-premises without relying on a public cloud. | Deploy in a platform-agnostic manner across any infrastructure. | Virtualisation, VM management, public/private cloud, OpenNebula. |
The b<>com method
Ongoing requirements analysis
Requirements are not set in stone at the start of the project. The team reassesses them at each key milestone to adapt the solutions to the reality on the ground.
A phased roll-out
First, ensure the deliverables are reproducible; then make them available to members. Each component is activated only when the project actually needs it, so that progress can be made quickly without compromising reliability.
Delivery prior to deployment
Each solution is tested, versioned and deployed directly into the customer’s or partner’s infrastructure, whether on-premises, in a private cloud or in a public cloud.
From the first prototype through to its roll-out on an industrial scale, the team is involved at every stage of the project.
Without a grasp of scalability, an AI project remains a prototype. Our role is to ensure that the solutions we build with you stand the test of time in production, within their real-world environments.
b<>com’s expertise at the service of your data and AI projects
b<>com develops innovative approaches to advanced statistical learning and generative AI that are directly tailored to the needs of its members. Every project begins with a rigorous analysis of your data, your constraints and your business objectives.
FAQ – Frequently Asked Questions
DevOps generally makes application deployment more reliable and automates the process. MLOps applies these same principles to machine learning projects: it introduces specific challenges such as monitoring models in production, detecting drift and tracking how they evolve over time.
A model is only as reliable as the data on which it is trained. Data that is poorly formatted, incomplete or unrepresentative jeopardises the entire project. b<>com gets involved at an early stage to build clean, structured and usable datasets.
It enables applications to be deployed regardless of the available infrastructure – whether a PC, a cluster, or a public or private cloud. The result is portable, scalable and easy-to-maintain solutions, with no dependence on a specific hardware environment.
From data preparation right through to production deployment. b<>com’s support is phased and adapts to the project’s milestones, ensuring that every component is in place at the right time.
Yes, an MLOps audit is particularly relevant for organisations that are just starting to deploy AI models into production – an area that is still relatively new for many organisations. A DevOps audit may also be carried out, depending on the maturity of the practices in place.
