Organizations that successfully brought AI models into production saw on average a 3% to 15% increase in profit margin, but up to 88% of all organizations are struggling to move these models past testing stages. Machine learning operations (MLOps) is the process of making sure your AI development is streamlined so that you can bring your models into production.
MLOps encompasses tools, best practises and processes that facilitates the end-to-end machine learning development process, which includes designing, building and managing machine learning software, with a focus on reproducibility, testability and evolvability of infrastructures. This means we’re not talking about one specific technology, but more about how multiple technologies work together, where they live and how they interact with each other.
It’s in your best interest to handle ML models with care. We always give our models a nice ecosystem to live in and thrive, so that they can do their work of making your life a better place. If you’re dealing with models that need to be retrained and updated often, in the long run, it’s a better investment to get MLOps right. Deploying your models and (re)training them automatically doesn’t only feel like magic, but will also make you more productive, and happier in the long run.
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