Tag: snowflake
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The Semantic Layer Is the Bottleneck
So many AI pilots follow the same arc. Week one: everyone’s excited, the demo works, the model answers questions correctly. Week four: the project is “paused” while someone tries to figure out why the AI told the VP of Sales that churn was 4% last month and told the CFO it was 11%, using the…
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Stop Asking the LLM to Do the ML’s Job
Before 2023, if you wanted a model to predict something, you had to build one. Gather data, pick features, choose an algorithm, train it, validate it, deploy it. That’s real work, and it’s work most business teams couldn’t do without a data scientist. Then LLMs showed up and could apparently do “AI stuff” without any…
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Snowflake ML Platform vs Custom Infrastructure: Why Most Don’t Need Custom
The $800K Platform You Probably Don’t Need I watched a Series B SaaS company spend $680K building a custom ML platform last year. Custom MLflow deployment. Airflow orchestration. Feature store built from scratch. The works. They had 2 data scientists and 20 models. The Snowflake ML platform would have handled everything for $180K. This isn’t…
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Building and Serving ML Models in Snowflake
Why Snowflake for Machine Learning? Let’s be real; a huge number of organizations have their data in Snowflake already. Moving that data out to train models somewhere else, then figuring out how to serve predictions back into Snowflake is a pain, especially when Snowflake’s machine learning capabilities let you build, train, and serve models right…