The lifecycle of the Machine Learning project involves different phases starting from the business team defining goals, setting metrics to Data Scientists building models, and the MLOps team deploying and monitoring machine learning models in production. After the model is ready for operationalization, either Data Scientist or MLOps team starts with the deployment of machine learning models to production.
The iterative nature of machine learning makes it harder to replicate the environments between development and production. Deploying Machine Learning models is not that straightforward as it involves not just the model deployment but also the data needed to train the model. Moreover, the iterative nature of building machine learning models require frequent retraining and validating models before pushing to production. Manually performing all these deployment steps are both time-consuming and labor-intensive.
AIQ Deploy empower enterprise to store models in any cloud or in-house registry, deploy models to any cloud-agnostic environment without having to re-engineer ML pipelines. Checkout how to promote models from experimentation phase to deployment workflows.
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