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It’s 2020, can I have my ML models now?

April 2, 2020 | Machine Learning

It’s 2020, can I have my ML models now?

Recent years have seen many companies consolidate all their data into a data lake/warehouse of some sort. Once it’s all consolidated, what next?

Many companies consolidate data with a field of dreams mindset – “build it and they will come”, however a comprehensive data strategy is needed if the ultimate goals of an organisation are to be realised: monetisation through Machine Learning and AI is an oft-cited goal. Unfortunately, before one rushes into the enticing world of machine learning, one should lay more mundane foundations. Indeed, in data science, estimates vary between 50% to 80% of the time taken is devoted to so-called data-wrangling. Further, Google estimates ML projects produce 5% ML code and 95% “glue code”. If this is the reality we face, what foundations are required before one can dive headlong into ML?

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