What’s the most challenging stage of the machine learning (ML) lifecycle? Data gathering and cleaning has traditionally been the most time-consuming aspect of data scientists’ and analytics practitioners’ jobs. But as solutions have popped up to address this issue, the bottleneck has moved to creating and deploying ML models.
The jury is still out on how many stages there are in the standard ML lifecycle, but for sure getting started is not a problem. Executives are throwing money at projects. Although a lot of projects have failed at the proof-of-concept phase, others have found success in identifying real business goals and establishing data science teams.
Actually building and evaluating machine learning models is the core stage of the ML lifecycle. But according to Algorithmia’s “2021 Enterprise Trends in Machine Learning,” once a use case is actually defined, it takes 66% of organizations more than a month to develop an ML model. For 64% of organizations, it takes at least another month to deploy that model.
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