Develop state-of-the-art Data Science products
Why ?
Your Data Science algorithms have been poorly developed or have not been maintained over time, or a change in your company's needs necessitates a revision of the product.
By improving the code of your AI products and the accuracy of the algorithms, the decisions you make become more reliable and informed. This leads to better performance for your company.
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Challenge
The quality of AI products directly impacts the underlying business processes.
Whether it's a product acting on commercial performance, customer relations, operational efficiency or regulatory compliance, the stakes are high.
Our experience has shown us that we can potentially double the performance of an artificial intelligence algorithm.
The quality of AI products directly impacts the underlying business processes.
Whether it's a product acting on commercial performance, customer relations, operational efficiency or regulatory compliance, the stakes are high.
Our experience has shown us that we can potentially double the performance of an artificial intelligence algorithm.
The expert
Antoine T.
Head of Data Science
Thanks to our expertise in Data Science, we guide you in improving your algorithms.
Our data experts will work hand in hand with your teams to improve your existing solutions:
Our customizable products :
Data Product Discovery
The data product discovery phase aims to mitigate risks related to value, business viability, usability, and technical feasibility of a data product in the most cost-effective manner possible.
This approach is based on hands-on experience to overcome client biases and focuses on the data product strategy by:
Utilizing a multidisciplinary team to address all risks.
Employing an iterative approach to deliver value from the first release.
Minimum Viable Product
In order to automate your production processes, we create a Minimum Valuable Product to allow you to quickly test the model in production:
Data Collection :
Definition of the challenge and scope of the use case, formalization of the company's needs
Analysis of key performance indicators and technical accessibility
Creation and analysis of the Dataset
Preparation for data ingestion and definition of the target architecture
Development of the model in an iterative mode
Model training: generate a new model based on a fixed data set, schedule the frequency of model training to account for new data, optimize the model in terms of resources consumed, response time, bias, and outliers.
Model inference: apply the model to a sample to get a prediction, forecast the refresh rate of the predictions, monitor performance: relevance of the obtained results, comparison with reality and business indicators. This step with a basic model and then with iterations.
Zoom Data Quality
Throughout the development of your solution, we implement data control processes to ensure its quality and business use:
Quality and Observability: Track data health end-to-end and eliminate its misuse by applying and automating freshness, volume, format, and business rules checks. Ensure the origin of the data through data lineage. Implement standards and testing tools at each stage.
Accessibility: Facilitate data usage by providing a full, unique, and harmonized version of the "truth". Implement a data catalog and discovery tools with the right level of access and control.
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