This article discusses causal inference, an emerging field in machine learning that goes beyond predicting what could happen to focus on understanding the cause-and-effect relationships in data. The author explains how to detect and fix errors in a directed acyclic graph (DAG) to make it a valid representation of the underlying data.
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
With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.
