Powering Manufacturing With MLOps
Machine learning is one of the most disruptive technologies across industries today. Despite this versatility and potential, many organizations struggle to capitalize on this technology’s full potential, especially in sectors like manufacturing that lack widespread ML skills and knowledge.
High upfront costs, complex deployments, data quality issues, and meager returns on investment (ROI) hinder manufacturing ML projects. If the industry hopes to implement this technology effectively, it needs a better approach to developing and using these models. MLOps offers an ideal solution.
As the name suggests, MLOps borrows heavily from the practice of DevOps, which now, according to Statista, accounts for 47% of software development projects. Just as DevOps marries development and operations to promote continuous integration (CI) and delivery (CD) in software development, MLOps applies CI and CD to ML model programming and deployment.