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When it comes to AI/ML, organizations often struggle with data quality (too little data, inconsistent data, or unusable data) and struggle to make progress on their own.
Artificial intelligence and machine learning are the future of digital products and data — and they don’t need to be an empty promise. RevUnit can help you uncover the value in your data to start thinking predictively.
Understanding data features, or the collection of input data to a machine learning model, is central to developing effective automation with machine learning. It’s impossible to attain quality results without clean, sensible features, so we:
Machine learning is complex and requires lots of experimentation to build the most successful model based on well-defined metrics. Model preparation is critical to successful automation.
A model that is not deployed is not providing value. Like software deployment, there is an entire art associated with AI deployment, and that can’t be overlooked when achieving successful automation.
Artificial intelligence combines computer science and robust datasets to enable problem-solving. It includes the fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines use AI algorithms to create systems that make predictions or classifications based on input data.
Machine learning is one subset of the field of artificial intelligence. It’s a way of solving problems without explicitly coding the solution. Instead, human coders build systems that improve themselves over time, through repeated exposure to sample data, which we call “training data.”
RevUnit takes an agile approach to artificial intelligence and machine learning. We build small, pilot models with test data — and then scale for accuracy.
AI/ML is the future of both data and product development. We can help:
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