Explores KitOps, an open source project that bridges the gap between DevOps and machine learning pipelines by allowing you to leverage existing DevOps pipelines for MLOps tasks.
ModelKits are standardized packages that contain all the necessary components of an ML project, including the model, datasets, code, and configuration files.
ModelKits are defined using a YAML file called a Kitfile, which can be integrated seamlessly with existing DevOps pipelines, much like a Dockerfile for containerization.
ModelKits are standardized packages that contain all the necessary components of an ML project, including the model, datasets, code, and configuration files.
ModelKits are defined using a YAML file called a Kitfile, which can be integrated seamlessly with existing DevOps pipelines, much like a Dockerfile for containerization.
In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
