This article provides a cheatsheet on the Infrastructure as Code (IaC) landscape, highlighting the benefits of scalable infrastructure provisioning in terms of availability, scalability, repeatability, and cost-effectiveness. It discusses strategies such as containerization, container orchestration, and tools like Terraform, Kubernetes, and Ansible. The article also introduces GitOps as a method for automating infrastructure updates through Git workflows and CI/CD.
Find and experiment with AI models for free, then switch to a paid Azure account when you're ready to bring your application to production.
- GitHub Models allows users to find and experiment with AI models for free.
- To find AI models, users can go to GitHub Marketplace and click on Models in the sidebar.
- The playground, available in the GitHub Marketplace, allows users to adjust model parameters and submit prompts to see the model's response.
- Users can compare two models simultaneously and are rate-limited.
- GitHub provides free API usage for experimenting with AI models in your own application.
- GitHub Models allows users to find and experiment with AI models for free.
- To find AI models, users can go to GitHub Marketplace and click on Models in the sidebar.
- The playground, available in the GitHub Marketplace, allows users to adjust model parameters and submit prompts to see the model's response.
- Users can compare two models simultaneously and are rate-limited.
- GitHub provides free API usage for experimenting with AI models in your own application.
GitHub Models now allows developers to retrieve structured JSON responses from models directly in the UI, improving integration with applications and workflows. Supported models include OpenAI (except for o1-mini and o1-preview) and Mistral models.
Kit is a free, open-source MLOps tool that simplifies AI project management by packaging models, datasets, code, and configurations into a standardized, versioned, and tamper-proof ModelKit. It enables collaboration, model traceability, and reproducibility, making it easier to hand off AI projects between data scientists, developers, and DevOps teams.
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.
We show you how to write a GitHub Action that queries a data-centric software system to generate an automated Slack notification and message.
Flux is a set of continuous and progressive delivery solutions for Kubernetes that are open and extensible. The latest version brings many new features, making it more flexible and versatile.
• 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
