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The first of which is data readiness. This includes having sufficient data thats been cleaner up for ML use. The second is the anticipated business impact what problem needs to be solved to either boost business efficiency or improve customer experience.

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If the basics arent in place, the talent hired to build the ML model will spend the majority of their time just filtering through data rather than building use cases.

We often notice what we call data hugging. There are multiple teams in every company working on different projects, and they get really attached to their data and they dont want to share their data with other groups, explained Anbazhagan.

This kind of data hugging can work in the short run allowing individual teams to roll out their little ML models, but in the long run, companies need to have an expansive view of all the data across their organisation. Advertisement

Picking a business problem to solve and using MLThis is the tricky part. According to Anbazhagan, some companies tend to either overshoot or undershoot. Ideally, the chosen business problem should have a huge impact, but it should also be something that the ML model can solve within six to 10 months.

Some companies over-index on the business impact and pick some really complicated problem for which there may not be a lot of data, he explained.Advertisement

Data readiness, business impact, and ML applicability are the three parameters that need to line up for a business to have a successful and robust ML strategy at the helm.

Addressing the ML skill gapDespite the growing demand for engineers and data scientists that specialise in ML, there is still a shortage in the market. Advertisement

This makes recruiting talent for ML applications difficult and expensive. Anbazhagan believes companies would be better served by developing internal talent instead and creating a community around reskilling as ML continues to grow.

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EXCLUSIVE: Amazon AI executive explains three things every business needs to address before using machine lear - Business Insider India