ML from Experimentation to Deployment
Anu Ganesan
March 17, 2021

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.

Follow us to learn about our ML tools automating end to end Machine Learning lifecycle from building, deploying to monitoring ML models at scale.

We hope you found our blog post informative. If you have any project inquiries or would like to discuss your data and analytics needs, please don't hesitate to contact us at info@predera.com. We're here to help! Thank you for reading.
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