MLOps: automate model training, deployment and retirement

Your business and AI must continuously learn and optimize to maintain high-performance. ​

Machine Learning Ops (MLOps) is a set of practices that connect data science with operations and make it possible for AI to integrate and align with business strategy. ​

These practices are responsible for harmonizing AI with your business and driving growth: enabling the technology to stay at scale with your business and vice versa.​ ​

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Avoid error: manually deploying models manually is susceptible to human error. MLOps automates model testing prior to deployment. ​​

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Consistency and redundancy (backup): by rigorously testing models, you can be confident that a new model won’t break or perform worse once in production. If this does occur, MLOps means it’s simple and fast to recall the new model and redeploy the old until evaluation can be completed.​​​​

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Cost: Unless you automate the entire ML process, this requires manual training and deployment whenever you want to update your model. This essentially means repeating the entire MLOps process each time. This significantly increases cost and is part of what MLOps was designed to avoid.​