Integrating & Automating the Training & Deploying of ML/DL Code Using Jenkins & Docker

Integrating & Automating the Training & Deploying of ML/DL Code Using Jenkins & Docker

MLOps : Integrating and Automating the Training and Deploying of ML/DL Code Using Jenkins and Docker. Step by Step Integrating Machine Learning and DevOps for training and deploying ML code with Jenkins and Docker Containers.

4 Steps Tutorial: Integrating Machine Learning and DevOps for training and deploying ML code with Jenkins and Docker Containers

MLOps is the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning. It is the creation of an automated environment for model development, model retraining, drift monitoring, automation of pipeline, quality control, and governance of a model into a single platform.

Any organization working with AI can automate data pipelining, model development, packaging, versioning, and monitor model accuracy. It can ease the work of data scientists and ML operations teams, enabling them to focus on higher-value creation.

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Integrate Machine Learning Models using Jenkins and Docker

From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker. There are many advantages to use Jenkins and Docker for ML/DL. One example is when we train a machine learning model, it is necessary to continuously test the models for accuracy.

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