How can I develop useful machine learning algorithms on complex data sets and models – without the heavy lift of engineering everything from scratch? Generative AI represents a step-change increase in the speed of analysis, but the utility of even the best GenAI tools are still constrained by the quality of the data and data models analyzed.
No one wants to build their data infrastructure from scratch. Thankfully open cloud standards (the “modern data stack”) and popular programming languages like Python and SQL give data teams a massive head start toward useful, actionable and ML-ready data. A growing number of commercial data integration tools like Fivetran, Talend, SoundCommerce, Matillion and Stitch offer users the ability to leverage and expand shared libraries of mapping and modeling logic, presenting the opportunity to greatly accelerate data time to value and analytics time to insights.
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