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AI stack attack: Navigating the generative tech maze
As enterprises grapple with the complexities of generative AI, many are gravitating towards comprehensive, end-to-end solutions.
Marvelous MLOps author Maria Vechtomova notes the industry-wide need for such simplification: “Machine learning teams should aim to simplify the architecture and minimize the amount of tools they use.” Tokarev Sela said, “We create a category of solutions which doesn’t exist yet.” Their approach aims to bridge the gap between data and business logic, addressing a key pain point in AI implementations. As we’ve explored the rapidly evolving generative AI tech stack, from end-to-end solutions to specialized tools, from data fabrics to governance frameworks, it’s clear that we’re witnessing a transformative moment in enterprise technology.
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