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.
Treating your infrastructure as code is becoming more and more necessary these days. Writing these instructions becoming challenging too. In Azure we use ARM templates to define the resources and associate them with a deployment pipeline. But ARM templates are quite complicated and they are not everybody’s cup of tea.
Azure MachineLearning Service provides four main compute options each with a specific purpose attached to it. In this post we will go through each of those and see where we can occupy them in your ML experiments.
In the era of Industry 4.0 where data and predictive analytics players the major role, developing machine learning pipelines have become an essential in intelligent application development.