The data scientists in our Data Insights team are adept at understanding business processes and problems through stakeholder workshops and requirements gathering to cut through the noise and formulate the simplest, most effective version of the problem statement to be solved.
With their strong academic and professional experience in designing and developing machine learning and deep learning solutions to solve said problem statements, they quickly identify a range of appropriate models to fit the data. A representative set of models that our Data Insights team has worked on is as follows (the left to right axis denotes ease of implementation, and the top to bottom axis indicates practicality of usage).
Further, our service offering doesn't stop at models. With the help of our machine learning engineers, we make sure that models aren't collecting virtual dust in a virtual corner. Instead, we help implement said models as modular, robust, and scalable microservices built using lightweight open source technology. And from model to service, we use the latest CI / CD tools and philosophies to ensure repeatability and faster turnaround time when the models need to be updated.
Finally, to close the loop, we show the proof in the pudding by designing A/B experiments (ranging from the simple Naïve Bayes to the dynamic multi-armed bandit) and implementing the same so that the correct and relevant metrics and outcomes are monitored, including through visualizations and dashboards.
With the explosion of data generation, machine learning is basking in the light of its zeitgeist moment, even as use cases powered by it continue to touch multiple aspects of our lives.
In 2018, a joint research project between the University of Central Florida and the City of Orlando used real-time traffic data to find strategies to help reduce car crashes and eventually improve road safety in the city.
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