Like DevOps, MLOps is gaining traction as an essential part of any machine learning set up.
Like DevOps, MLOps is gaining traction as an essential part of any machine learning set up. Practising MLOps means that you advocate for automation and monitoring at every step of ML system construction, including integration, testing, releasing, deployment and infrastructure management. Data scientists can implement and train an ML model with predictive performance on an offline holdout dataset and given relevant training data for their use case. However, the real challenge isn't building an ML model. The challenge is building an integrated ML system and to continuously operate it in production.
We helped deploy one largest and most successful MLOps deployments in Australia, Kodez aims to unlock the benefits of MLOps to other customers.
In this exercise we used;
We worked closely with the customer implementing various components in the MLOps pipelines and infrastructure.
As enterprises build their business vision around intelligence, the need for machine learning intensifies. Work with Kodez, and we will bring added reliability and optimization to machine learning models.
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