6 Ways Open Source Can Augment Your Advanced Analytics
Many organizations like yours already understand the power of AI and the important role it plays in staying relevant in their industries. In one recent McKinsey survey, 47% of respondents said that their companies have embedded at least one AI capability into their business processes vs. just 20% in 2017. The shift to becoming AI-driven is already happening in organizations across the globe.
For many companies, this means embracing the innovation, flexibility, and transparency that open source AI tools offer to data scientists alongside existing advanced analytics systems they continue to depend on. But how do you build that bridge between the two systems?
Our white paper, 6 Ways Open Source Can Augment Your Advanced Analytics, highlights how AI-driven companies can successfully bridge the gap between new open-source AI solutions and legacy technology.
Download the white paper now to learn:
- How legacy analytical methods can jeopardize innovation and competitiveness, stifling progress for the sake of processual stability
- How companies can augment existing teams to deliver more AI projects
- How teams can scale machine learning productivity today, without relying on legacy software
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The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.