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Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.
Automate traditional systems to take care of the heavy documentation, reporting, and other administrative processes that take place within a government office and save time and human effort in maintaining such records.
Keep track on a region’s health using smart AI systems that can detect any signs of spreading of disease early on by looking at the data from hospitals and can help government bodies manage pandemics better.
Deploy ML models that use the city’s fleet of surveillance cameras to detect areas that look suspicious or facially recognize convicted offenders, to handle and manage illegal activities more easily.
Optimize a city’s traffic routes using AI techniques and design roads in such a way that the more congested zones are freed and the traffic flow is continuous.
Building and maintaining such complicated systems will always require expertise within the ML field and the cost to hire can go through the skies. Also, training, deploying, and monitoring such ML models is expensive and cumbersome.
The data from various sources needs to be managed and processed properly. Also, a lack of insight into the data’s quality can lead to a model that produces illogical results leading to a bad performance in the real world.