How Machine Learning Transforms Data Quality And Operational Necessities

Machine Learning has the tendency to evoke extreme reactions – some consider it a superpower while for others, machine learning is just another fad. That said, research has shown that 76% of enterprises have prioritized AI and Machine Learning initiatives over other IT projects. One in every 10 enterprises now uses multiple AI applications in the form of chatbots, fraud analysis, etc. It’s important to note though that Machine Learning can play a critical role in automating business processes only as long as certain prerequisites are met.Operational Prerequisites For Machine Learning models to be successful, the program must suit the context and it must meet certain criteria. Let’s look at some of the basics.First, There’s The Scope Of The Project To Be ConsideredMachine Learning excels at finding patterns in databases and detecting insights from these patterns. This makes it suitable for projects with specific goals. For example, it helps assess the sentiment behind product reviews and uncovering market trends. Machine Learning can also be quite effective at predicting stock prices. But if the project scope is too wide or the datasets are too big, Machine Learning may become very expensive and time-consuming to maintain. Manual Biases Can Also Manifest A ...


Read More on Datafloq