Iterative modelling leading to automated automation…
Machine learning can be a tough subject. First of all, data scientists need to design appropriate algorithms to carry out an intended task. They then need to train it using relevant data. Different scenarios require different hyperparameters – elements of value that exist before the machine learning process. Without optimal hyperparameters, quality data and the right algorithm, machine learning won’t perform as required.
Data scientist Sebastian Raschka, calls machine learning ‘the automation of automation’ where a computer program carries out a task, and machine learning lets the computer automatically optimise performance. Automated machine learning (AutoML) takes this one step further by allowing computers to analyse the accuracy of the machine learning algorithms themselves, and automatically improve them through iterative modelling. This means that algorithms themselves will be able to automate their own performance assessments and improvements.
Machine learning isn’t becoming more effective, as is AutoML. However, by automating the labour intensive job of building better algorithms, it is vital to the development of data science and AI. By delegating algorithmic analysis to AutoML, data scientists can concentrate on less straightforward tasks that need human input and application knowledge. It completes data work that would be almost impossible without advanced data skills, making machine learning more accessible. Through AutoML, organisations can build their own accurate algorithms, adopt transformational DevOps strategies, and move towards intelligence driven enterprise.
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