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I really liked the points you made about end to end machine learning development, especially how having a full workflow from collecting data to deploying and maintaining models helps businesses get more consistent value from ML instead of treating it as a one-time project. A lot of companies still overlook how important continuous monitoring and updating are, especially when their data keeps changing. Even industries that rely on structured processes, like manufacturing or engineering, can benefit from integrating ML in a way that supports daily operations rather than adding complexity, much like how tools from 5thAxis.com help streamline precision and consistency in their own domain.in your experience, do smaller businesses have the capacity to adopt full ML pipelines effectively, or is this approach still more practical for larger organizations?