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Updated: May 1, 2023 About the authors: Mike Del Balso, CEO & Co-Founder of Tecton Willem Pienaar, Creator of Feast Data teams are starting to realize that operational machine learning requires solving data problems that extend far beyond the creation of data pipelines. In a previous post, Why We Need DevOps for ML Data, we highlighted some of the key data challenges that teams face when productio
Getting machine learning (ML) into production is hard. In fact, it’s possibly an order of magnitude harder than getting traditional software deployed. As a result, most ML projects never see the light of production-day and many organizations simply give up on using ML to drive their products and customer experiences.1 From what we’ve seen, a fundamental blocker preventing many teams from building
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