WE SOLVE THE TOUGHEST HUMAN PROBLEMS
THROUGH THE TRANSFORMATIVE POWER OF
ARTIFICIAL INTELLIGENCE

Select

There are 40,000+ Open Source AI models and architectures with unique settings and parameters that can be applied to your business problem.

Floating Point platform enables fast iteration and testing of models to discover the best configuration for solving your specific use case.

The platform allows for repeatable and measurable experiments to determine the optimal solution for your business problem.

Matt Kidd, Senior Data Scientist, walks you through our process of selecting the correct models for your AI project

Prepare

During preparation, the Floating Point platform takes in your raw data sets and external data, transforming them into suitable formats for analysis and modeling.

• Data Ingestion: We import the relevant data into the platform in the appropriate format
• Data Processing: We understand the data to be able to put it into the correct format for training the models
• Annotation: If necessary, we annotate the data to create a training data set for supervised learning

Leticia Fernandes, Senior Data Scientist, shows you how we prepare our models

Train

The Floating Point platform is both Model-agnostic and Cloud-agnostic, allowing us to choose the optimal infrastructure for your project.

We can monitor the current training process and review past experiments.

This approach enables us to replicate each step of the process, understand model improvement, and foster continuous innovation and iteration in training.

Dr Catarina Carvalho, Senior Data Scientist, explains our model training methodology

Integrate

The Floating Point platform offers both Cloud and On-premise integration.

Cloud integrations are suitable for companies lacking experienced data engineers, while On-premise integrations cater to clients with specific data privacy and legal requirements.

Once we select the best model from the experiments, based on your agreed success metrics, then we promote it to production and integrate it. The platform is highly scalable, automatically adjusting to meet your business needs in high demand scenarios. This ensures fast and cost-effective model execution in production.

Diogo Ribeiro, Senior Machine Learning Software Engineer, walks through our model integration process

Manage

The Floating Point platform allows us to monitor and manage the AI solution after deployment and while it is in use in the real world. We are able to monitor for data drift over time and to check for any errors in the accuracy of our model over time.

Our dedicated support engineers can monitor, compare and prepare future versions of your model. We are able to monitor for data drift and to check for any errors in the accuracy of our model over time.

The platform enables close monitoring of usage, costs, and scalability aligned with your business needs. It ensures contracted uptime and a high level of service.

Dr Tom Heseltine, CTO, walks you through our process of selecting the correct models for your AI project