Source: medium.com
Integrating Machine Learning with DevOps ToolsCategory: Jenkins, github
In this article I will give the detailed insight of Task-3 given by Mentor Vimal Daga Sir during my MLOps Training under his guidance. Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows.
MLOps applies these principles to the machine learning process, with the goal of: MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models. The task description of this task is as follows: Create container image that’s has Python3 and Keras or numpy installed using dockerfile When we launch this image, it should automatically starts train the model in the container.
For creating the image we have to rum the command in the same folder in which the Dockerfile is present.
MLOps applies these principles to the machine learning process, with the goal of: MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models. The task description of this task is as follows: Create container image that’s has Python3 and Keras or numpy installed using dockerfile When we launch this image, it should automatically starts train the model in the container.
For creating the image we have to rum the command in the same folder in which the Dockerfile is present.
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