How AI can take the drudgery out of tuning machine-learning models paid Oracle
Cloud computing has made it so that artificial intelligence and machine learning are now tools that almost any company can use to find practical answers to difficult business problems. As companies scramble to get a competitive edge using AI and machine learning, they’re learning an important lesson: these aren’t “one strategy fits all” technologies.
AutoML takes over the often labor-intensive job of choosing and tuning machine-learning models. While there is no substitute for skillful problem definition and data preparation in the machine-learning process, AutoML takes on many of the repetitive tasks, reducing the need to understand algorithm parameters and shortening the compute time needed to produce better models.Let’s back up a step to look at how AutoML fits into the larger machine-learning picture.
There are many well-known algorithms for machine learning, and it’s not always obvious which algorithm will work best for building real-value prediction, anomaly detection, or classification models for a particular data set. In complex, real-world situations, a data scientist may need weeks or months to choose the right algorithm and refine the model created using that algorithm.
Lastly, when tuning how the chosen algorithm works, the process called “hyperparameter tuning” involves lots of trial and error. Complex ML algorithms can have over a dozen configurable parameters, and each of these parameters can have a large impact on model performance.
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