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Refers to achieving enterprise-wide participation in the AI development, test, and deployment process.
Enterprise-wide collaboration is only possible with the elimination of complex semantic programming skills such as R or Python. A no-code environment increases transparency which leads to greater confidence across all stakeholders thus, maximum adoption.
With ongoing global political unrest, erratic weather patterns, and pandemics, we have seen a paradigm shift in how a business operates; thus, doing things based on past experiences or practices will be problematic.
Data-driven AI develops predictive and prescriptive models that leverages all relevant data from both internal and external sources and irrespective of its format to inform and improve the performance of AI projects.
Multi-persona AI simply refers to AI designed to mimic the behaviour of multiple people or “personas.” This can be achieved in several ways, including using multiple models or algorithms to simulate different personalities or training a single model on data from multiple individuals.
By simulating multiple personalities, these solutions can respond to user input in a more varied and engaging way rather than simply providing pre-determined responses, thus significantly increasing the adoption of these solutions.
Genetica’s Cortex Cognitive AI Platform is a cloud-based end-to-end AI development & lifecycle management solution.
Cortex provides an intuitive UI which requires zero programming making it ideal for developing, deploying, and maintaining industrial-strength AI applications
Predictive models run autonomously, leveraging real-time data feeds from IoT devices, alerting different users at different times with relevant information and recommendations for their specific roles.
Genetica reduces both the cost and time to deploy by a factor of ten.
A fully open source, distributed in-memory AI development & lifecycle management solution.
It operates as a self-organizing computing cluster with a high degree of linear scalability.
A runtime environment built for data-intensive, real-time applications.
Our platform facilitates enterprise-wide participation in the development of AI applications.
Whether you are a data scientist, data engineer, data analyst, or line-of-business manager, our intuitive platform provides access to everyone to ensure the success of your AI projects.
Enterprise Collaboration & Adoption
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The simple reality is for your models to be effective, they will need lots of data for training and testing. This data will reside in multiple internal and external systems in your organization. The data will also be in various formats, including real-time streaming. Genetica’s platform allows for easy acquisition of multiple data files and then provides the ability to transform and consolidate data for optimal model training intuitively.
Before training your model its critical to ensure you have the right data. Missing data or more importantly biased data will significantly reduce model accuracy. The Genetica platform has prebuilt reports that provide a comprehensive visualisation of you data.
The Genetica platform provides a comprehensive range of modelling options of Neural Networks, Decision Trees, Survival Analysis and multiple forecasting capabilities. Building even the most complex neural network is an intuitive ‘no-code process’ its simple drag n drop functionality.
Training and Testing a model is again a intuitive co-free process. The platform will shuffle and split the data into separate training and datasets; then run the appropriate number of epochs to ensure optimized model results. Users are provided with a comprehensive set of reports that allow technical and non-technical stakeholders to analyze the training assumptions and process. Users are also provided an ability to demo the model to confirm its applicability
Once a model has been trained and tested and users are happy with the models accuracy deployment of the model is a one click process. comprehensive visualisation of you data. The platform will create and executable file that can be deployed in via the Genetica mobile portal or imbedded into any existing client operation systems.
Deploying a model is one thing ‘Operationalizing’ it is something quite different again. For a deployed model to provide stakeholders maximined benefit it need to run seamlessly in the background leveraging ‘machine intelligence’ which involves accurately perceiving, utilizing, and managing its environment.
Sitting on top of layers of services, components, rules, and predictors our Guardian module understands the “what" needs to be done and handles the “how” and by whom internally.