Before developing a product or feature, it’s essential to focus on your pain point and figure out the value proposition that users can get from it.
It’s more critical to get high-quality data than to spend time on improving the AI model itself.
It’s necessary to create prediction or classification machine learning algorithms so the AI model can learn from the dataset.
We establish model accuracy by setting minimally acceptable thresholds.
We can go for an in-house or cloud framework. The choice depends on the application.
There is more than one programming language, including the classic C++, Java, Python, and R. The latter two coding languages are more popular because they offer a robust set of tools.
By monitoring the system(s) after deployment, we can ensure it’ll keep performing well. The operation will need monitored constantly.
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