potential and widespread hype, machine learning and artificial intelligence analytics do not always live up to expectations when deployed in business. This is often due to factors such as irrelevant or misaligned use cases, poor data governance and a lack of synergy between business and data science teams, according to the Knowledge Integration Dynamics data science experts.
ML application has a different objective to that of pipelining and executes complex learning logic models not only to support analytics, but also AI. Both sides need to have an understanding of the business domain and data science, Van Niekerk said. “ML projects are not one size fits all undertakings and require many different skill sets.”
“And lastly, the new generative AI capabilities of the platform adds a new dimension to model interpretation, as well as code generation for further model customisation,” Charters said. The KID team agreed that defining and sharing responsibilities helps ensure maximum ROI. Top said: “Teams should align their objectives with the overall business goals. Data science projects should directly contribute to solving business problems and achieving strategic outcomes.”
To ensure that the end-users have trust in the models, Top recommends that organisations create mechanisms for continuous feedback. “Business teams should provide feedback on the usefulness of data science insights, and data scientists should iterate on their models based on this feedback,” he said. “Organisations should select and implement the right tools and technology to facilitate collaboration.
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