Important prerequisites to innovate with Machine Learning are fast prototyping, standardised development life cycle, production capability and the scaling of Machine Learning solutions. Innovative organizations incorporating data-driven decision-making often encounter the challenge that the prototyping, development and production environments of machine learning solutions lack coherence and consistency.

Constrata provides the synergy between all the components of the Machine Learning Model Life Cycle and Software Development Life Cycle. The Constrata managed platform enables organizations to become data driven decision makers by empowering their data scientists with relevant and easy to use tooling configurations. Constrata defines a best practice way of work that is proven in use and can easily be adopted by organizations to serve their strategic objectives.

Microsoft Azure secures the computational and information processing capabilities to facilitate the implementation of large Machine Learning models and to render the results at enterprise scale.

At the heart of the Constrata platform is an all-inclusive eco-system of features and tools. This enables data scientists to do fast prototyping and take the solution through all the development phases to a full production-ready solution. The platform encapsulates the whole technology stack while enabling data scientists to implement customized solutions.

Our platform will enable the identification of research problems, acquire and clean the data from structured and unstructured sources, support the Machine Learning model building process, test and validate the models and launch the crafted solutions. Constrata ticks all the boxes:

Data access
Data preparation
Automation
Machine Learning
Domain agnostic
Delivery
Flexibility
Scalability
Model management
Coherence
Data exploration and visualization

Praelexis Constrata Data Science Platform

作者 Praelexis