A Docker container for quickly standing up a Splunk instance, complete with Eventgen and Splunk's Machine Learning app for testing and training purposes.
This article explains how to run inference on a YOLOv8 object detection model using Docker and create a REST API to orchestrate the process. It includes code implementation and a detailed README in the author's GitHub repository for running the API via REST with Docker.
This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
• 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
tensorflow jupyter):
Download the training zip file from drive, extract it
docker run --rm -it -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 -v /Users/foo/Learn/python/training:/home/jovyan/ jupyter/tensorflow-notebook:latest
Download the training zip file from drive, extract it
docker run --rm -it -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 -v /Users/foo/Learn/python/training:/home/jovyan/ jupyter/tensorflow-notebook:latest
