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Machine learning is transforming the way businesses look at data and presenting new analytics opportunities for companies of all sizes. How firms leverage new technologies for machine learning in the industry will be a vital deciding factor in riding the waves of changes. Machine learning algorithms are deployed in various sectors, and tracking the development of it throughout its life cycle is crucial.
The deployment of machine learning models involves a training phase, where a data scientist designs a model with excellent predictive capability based on historical information. This model is put into production and is expected to have a similar predictive performance during its deployment. There can be issues associated with the information deployed in the model such as incorrect models getting pushed, incoming data being corrupted, and incoming data no longer being resembled datasets used during training.
At the heart of the ML model, is a Server and an Agent. The Server keeps a record of all the deployments across agents. Users can leverage built-in applications, health metrics, create new forms, or import their existing ML pipelines and computations using any of the popular programming languages.
Machine learning model performance is, and ideas of what score an excellent model can achieve can be interpreted in the light of the skill scores of other models also trained on the same data. As a machine learning model performance is relative, it is crucial to create a robust baseline. A baseline is a simple procedure for making predictions on the predictive modeling problem.
It is hard to find a model that can demonstrate works reliably well in making predictions on a firm’s specific dataset.
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