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In many cases, AI projects in enterprises are approached in the same way as other IT implementations, where focusing on selecting an experienced vendor and creating a precisely defined proof of concept are the key milestones. Projects carried out in this way usually end in the PoC phase and never reach deployment. The search for efficient solutions using data science or machine learning requires a different approach.
At deepsense.ai we work on the basis of CRISP methodology, a process model including six stages of running AI projects that lead to the right solution when repeated over and over again. This concept involves an agile approach, where priorities and pathways change as project development progresses.
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