Schedule a call with us to determine the feasibility of AI/ML project.
Our team of data scientist and engineers work towards solving the challenge. Please rest assured that you are in good hands.
The last stage, when we transfer the knowledge to you team, to manage and maintain the system.
In the real world, data is highly interconnected.
Today, most data science analyses revolve around aggregation and generalization. But we live in a hyperconnected world and personalization is needed at every stage of the customer lifecycle. For this reason alone, we have developed ML-Graph a scalable graph analytical solution.
Automatically generate high-performing intelligent alternative strategies that you'd never think of on your own from a single idea.
With our proprietary generative rule mining solution, you'll be up and running quickly and start developing non-hypothesis data-driven business strategies.
As the name implies, time series data is an ordered sequence of a particular event spaced over time. In the real world, it is used to process clickstream data, financial analysis, and sensor data. These sequences contain valuable information that can be leveraged to build intelligence.
Modeling time series data is challenging because cross-sectional or temporal aggregated views of the data effectively eliminates signals in the fundamentally ordered nature of the data.
To solve these changes, we have built a deep learning Hydra Model architecture to divide-and-conquer different types of data in different "heads", and then synthesize them to extract intelligence and assist decisions.
The stream of events, in particular, are represented as a sequence sorted in time.
For example, sequence of webpages visited by a user, sequence of transactions on an account, sequence of doctor office visits.
A hydra model divides-and-conquers different types of data in different "heads", and then synthesizes them to extract intelligence and assist decisions.
During real-time streaming, the hydra model evaluates and provides recommendations for the "Next Best Action". These recommendations can then be incorporated into real-time decision making at scale.
Models are built on the character, ability, strength, and truth in Data, algorithms, and People.
Transparency about data origins, machine-learning intentions and model explainability
Diversity is required in people's minds, backgrounds and cultures, as well as data selection and algorithm choices.