Develop state-of-the-art Data Science products
Why ?
Connected or not, in the cloud or in a complex industrial environment, the advancements in Computer Vision models in recent years, coupled with interconnectivity capabilities with industrial machines, offer new use cases that were previously impossible not long ago.
Developing descriptive, predictive, or prescriptive models will help you identify trends, reduce bottlenecks, improve product quality, and find growth opportunities for your company.
Contact-us.gif?width=600&height=338&name=computer%20vision%20gif%20(2).gif)
Challenges
Computer Vision enables the automation of tasks that were previously partially or entirely done manually. Today, quality inspection of products or production line inspection can be carried out by computer vision systems. This helps reduce errors, improve quality, or prevent industrial risks.
It can also aid in building new innovative products or services to help you compete against new entrants or enter new markets.
Computer Vision enables the automation of tasks that were previously partially or entirely done manually. Today, quality inspection of products or production line inspection can be carried out by computer vision systems. This helps reduce errors, improve quality, or prevent industrial risks.
It can also aid in building new innovative products or services to help you compete against new entrants or enter new markets.
The expert
Antoine T.
Head of Data Science
With our expertise in Data Science, we are committed to guiding you in the development of your Computer Vision solutions.
Our data experts will work hand in hand with yours to deploy and industrialize Computer Vision algorithms :
Our customizable support
MVP Data Sciences
In order to automate your production processes, we perform the Minimum Viable Product (MVP) to allow you to quickly test the model in production:
Data Collection :
Define the challenge and scope of the use case, formalize the business needs.
Analyze key performance indicators and technical feasibility.
Create and analyze the dataset.
Data ingestion preparation and definition of the target architecture.
Model development in an iterative manner
Model training : gGenerate a new model based on a fixed dataset.Plan the frequency of model training to incorporate new data. Optimize the model in terms of resource consumption, response time, bias, and outliers.
Model inference : Apply the model to a sample to obtain a prediction. Determine the frequency of prediction updates. Monitor performance: relevance of the results, comparison with reality and business indicators. Iterate this step with a baseline model and subsequent iterations.
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.
.jpg?width=1920&height=1000&name=Vignette%20e%CC%81tude%20de%20cas%20%20(14).jpg)
.jpg?width=960&height=500&name=Vignette%20e%CC%81tude%20de%20cas%20%20(3).jpg)
We are the organizers of Meetup Computer Vision Paris, which was founded in late 2017. Each season, the meetups provide an opportunity to discuss technical challenges related to Computer Vision that data scientists and decision-makers may encounter. The CV Meetup aims to address technical problem-solving topics, present state-of-the-art research, and delve into the various use cases enabled by Computer Vision.
A thesis in Few-Shot Learning. Sicara welcomes Etienne, who is conducting his thesis on Few-Shot Learning as part of his doctorate at Centrale Supélec.
%20(1).png){kind=logo}
48 Bd des Batignolles, 75017 Paris