Design and deploy ML models for predictive analytics, automation, and smarter customer experiences.
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We design and build powerful, tailor-made ML solutions that extract unparalleled insights from your data. Whether you're looking to automate, predict, or personalize, our expertise in AI development delivers measurable impact, transforming challenges into opportunities and setting you on a path to sustained innovation.
We develop software on Microsoft Azure, AWS, Google Cloud, and open-source platforms. This flexibility allows us to adapt to each client’s needs - whether that’s an enterprise-grade ecosystem like Azure or more open-source-friendly options such as AWS and Google Cloud. Our dedicated expert teams specialise in each platform to ensure the best results.
The product backlog is a list of project goals and contains what is forecasted to be developed by the development team, and maintained by the Product Owner. It is a living document, updated continuously, prioritized, and ordered by business value. It may also have product improvements, bugs, technical questions, and so on. Its purpose is mainly to have everything that is needed to reach the project’s Product Vision.
In this stage, we also create a sprint backlog, which is a list of tasks that need to be completed during each sprint. We prioritize the user stories for each sprint and ensure that the team knows what they need to work on.
With the sprint backlog in place, the development process finally starts - sprint execution. Working through the sprint backlog, and delivering small usable pieces of software frequently allows for continuous feedback and refinement, ensuring that the product is always on the right track.
In this stage, we put in place a briefing that includes the information gathered during the workshop with the team and stakeholders. It presents the vision and goals of the project and clarifies all necessary business requirements. This is also where an FAQ session relative to the nature of the project takes place.
The high-level architecture involves the development of the technical design, with the ideal balance between complexity and reach, This is where we identify external dependencies from third-party providers, such as Stripe, Facebook, Amazon, and so on.
We then start the CI/CD workflow which is the setup of the issue management tool, code repositories, continuous integration system, and development & staging environments. It’s followed by the setup of the code repo and automated test framework, the staging environment and production servers, as well as the continuous integration ecosystem (i.e. servers, deploy hooks, etc)/continuous deployment.
Finally, in Feature 0 we deliver the first meaningful feature: a homepage, a login screen, part of the first dashboard… This step ensures that there is something demonstrable with the perception of value at the end of the Bootstrap Phase.
With the Data Model, we provide the first baseline of the product’s evolutive data model. It identifies the main data entities and relationships and baselines the data sources and data stores (i.e. relational databases, document data stores, etc.). This step also consists of iterating the product concept and designing the first version of the data model.
Here’s when we present the Proof Of Concept (PoC), Minimal Testable Product (MTP), or Minimal Viable Product (MVP), and we deliver and deploy the first version of the product - even if that version is the implementation of a concept. This helps mitigate technical risks and test the main business premises for developing a market-ready version through a viability assessment.
On the production increments step, we review technical and business risks and the impact of the PoC, MTP, or MVP on the initial premises or Wave 0. Here, we also identify reusable components from Wave 0 to Wave 1 (i.e. often PoCs are not reusable). This helps gather feedback about the first integrated model and assesses the product's viability before moving to Wave 1.
Finally, the wave retrospective reviews the product state, evaluates wave success against business goals, and identifies improvements. We then design goals for the next wave & prioritize features. Doing this allows the team to analyze the work that has been done in previous sprints and plan consciously what should be the next mountain to climb.
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Our business developers, project managers and software engineers can help you to clarify any questions you have related. Feel free to chat with us anytime and get a quote for your project.
ML models need high-quality data, which many businesses lack. Imaginary Cloud supports data collection, enrichment, and synthetic data strategies.
Models degrade over time without monitoring. We set up continuous training pipelines and monitoring dashboards to keep models up to date.
Manual data labelling slows progress. We streamline this with automated pipelines, annotation tools, and semi-supervised learning methods.
Lack of explainability limits adoption. We prioritise interpretable models and deploy explainability frameworks.
Poorly generalised models erode trust. Our ML engineers use robust validation, regularisation, and fairness checks to ensure reliable performance.
Many ML models stay stuck in the pilot stage. Our MLOps practices ensure models move efficiently from prototype to production with CI/CD pipelines.
TOP SOFTWARE DEVELOPMENT
Goodfirms
TOP SOFTWARE DEVELOPERS
Clutch, 2023
TOP SOFTWARE DEVELOPERS
Acquisition Int., 2023
Bridging borders, serving clients in over 80 countries worldwide.
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With a 99% client satisfaction rate, Imaginary Cloud brings together a handpicked team of EU-based data science experts trusted by global enterprises. From machine learning engineers to cloud-certified data architects, our specialists combine deep analytical skills with a business-first mindset.
We deliver intelligent systems that are robust, explainable, and ready for scale. Whether you need end-to-end product delivery or a seamless extension to your in-house team, we lead every stage with speed, rigour, and strategic clarity.
Data Scientists
Experts in statistical modelling, machine learning, deep learning and optimisation. We translate business challenges into model-driven strategies.
Data Engineers
We build reliable pipelines and scalable infrastructure to ensure clean, timely and structured data for modelling.
ML Ops Specialists
Our team ensures that your models are reproducible, traceable and production-ready, with full lifecycle monitoring.
Project Managers
Agile-certified PMs ensure that data projects are delivered on time, within scope and with measurable impact.
Browse the Frequently Asked Questions and get your answers. Or better yet – get in touch with our team and let’s talk!
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Our team of business developers and project managers can help you to clarify any questions you have related. Feel free to chat with us anytime.
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