How we work

We deliver value to you at each step of a standard Machine Learning workflow.

Our way of work is highly iterative. We frequently gather feedback from business stakeholders, to make sure that parameters of the project and business objectives are aligned and enhanced with each iteration.

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Machine Learning education workshops

Beyond the buzz words, our team meets your team to explain the core concepts around AI and how it works in practice. Once demystified, you will be able to understand how Machine Learning can make your work easier and more efficient.

The objective is not to make you a ML engineer but to show you the capabilities of ML and its fields of applications.

Ideation workshops

Do you want to introduce and leverage Machine Learning in your organization quickly and effectively? If so - contact us for an ideation workshop to discover where Machine Learning can take you!

We will start from your business needs and together discuss how technology can be of help. We will guide you to focus on business aspects where Machine Learning will be a key differentiator and have a high impact on your business.

The objective is to define potential pilot projects that could be launched quickly in order to get early feedback on ML benefits.

Business analysis

We start with defining expected results and impact of the project, together with understanding the specific constraints of your business. Following, we build a map of the available data and third-party data sources that are helpful in solving the specific type of problem targeted in the project. Finally, based on the available resources and constraints, we propose a set of techniques that are most likely to be successful for the project.

Team and process

Together with you, we set the right targets to be reached.
Then, we align to set up the right project team and the right stakeholders for achieving your Machine Learning objectives.

Data exploration,
preparation and model development

Machine Learning Project From A to Z

The quality of your input determines the quality of your output. Thus, we extract, explore, transform, and enrich raw data into clean and structured data. Then, we construct the data sets. These steps are critical to avoid unexpected problems during the next phases or the project.

The next steps in our workflow are to develop a high-performance Machine Learning model by choosing the right technique(s), choosing optimal hyperparameters, training, and evaluating the model. It may be needed to iterate over these steps to achieve your business objectives.

Deployment at scale

In this step, we deploy the best model at scale so it can handle 10, 10K or 10M concurrent predictions on real data.

Monitoring

Machine Learning models should continue to deliver the expected level of performance, even when the input data may degrade over time. To ensure this, we continuously monitor the solution performance, and deploy automated infrastructure to rebuild fresh models, using new data.

From model building to operating in production

MLOps

DevOps has become standard for IT operations and Cloud services. Since there is powerful synergy between DevOps and Machine Learning, we can bring the lifecycle management of DevOps to Machine Learning for its greater benefit.

This way, MLOps lifecycle management emphasizes on continuous delivery, monitoring, and automation pipelines to build and manage your models.

FinOps

ML projects can require extensive computing resources. In synergies with our Cloud Centre of Excellence, we promote a cloud-first approach for ML project in which we optimize the resources costs at every step.

Customers with a private Cloud solution also benefits from our approach to define and tune the infrastructure requirements of their ML projects.