'Approaching AI Model Deployment the More Efficient Way' by getmodzy modzy artificialintellingence
The last component of ModelOps and MLOps pipelines is the production deployment stage. This stage occurs after a model is trained and reaches a suitable level of performance and is ready to make predictions against live data. On average, it takes organizations about nine months to deploy models into production.
The skills gap required to build AI-powered solutions and software systems is one of the major drivers of the AI Valley of Death. Only a fraction of production ML systems are composed of actual ML code. As a result, data scientists are left facing the daunting task of building a production system without the skillset to do so. This often leads to quick and cheap solutions, which ultimately generate significant technical debt, costing organizations more in the long run.
The only pre-requisite to leveraging Modzy’s different deployment options is that all models must be containerized to meet the open-source container specification. This container specification comes in a few different flavors – gRPC protocol, REST protocol, or Open Model Interface – each providing data scientists the flexibility to choose what fits best for them.After you have containerized your model to meet one of the above specifications, save your container image as either a .
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