AI faces its biggest ‘decision’ yet

Artificial Intelligence and its ability to perform different strains of Machine Learning functions is arriving at the point where it will need to make the biggest ‘decision’ of its life so far - can learning itself be automated to be leaner, keener and compute-resource-greener?

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Artificial Intelligence (AI) and its ability to perform different strains of Machine Learning (ML) functions is arriving at the point where it will need to make the biggest ‘decision’ of its life so far.

That decision is all about how AI (and the ML functions it drives) should come to its predictive decisions i.e. how extended and complex should the software engineering be behind the decision-engines and, crucially, how much ML automation should be introspectively applied back upon itself to make the learning power the learning?

Machine Learning Operations (MLOps) and DevOps solutions architect at Git Lab Monmayuri Ray explains that ML represents a shift in the way we predict and make decisions.

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