Fine-tune models - Visual Studio Code
Use custom dataset to fine-tune a generative AI model in the Azure cloud or locally with GPUs. Deploy the fine-tuned model to the Azure cloud or download incremental files from fine-tuned model.
AI Toolkit for Visual Studio Code
AI engineers can use AI Toolkit to discover and try popular AI models easily with playground that has attachment support, run multiple prompts in batch mode, evaluate the prompts in a …
Run multiple prompts in bulk - Visual Studio Code
Run a set of prompts in an imported dataset, individually or in a full batch towards the selected genAI models and parameters.
Settings Sync - Visual Studio Code
Settings Sync lets you share your Visual Studio Code configurations such as settings, keyboard shortcuts, and installed extensions across your machines so you are always working with your favorite setup.
Models in AI Toolkit - Visual Studio Code
Find a popular generative AI model by publisher and source. Bring your own model that is hosted with a URL, or select an Ollama model.
AI Toolkit playground - Visual Studio Code
Chat with selected generative AI model in playground. Change system prompt and parameters. Add attachment for Multi-Modal models. Keep chat history.
Azure Extensions - Visual Studio Code
Deploy data processing and machine learning models in containers using Azure Container Apps, creating scalable and reproducible environments for your data-driven applications. Azure Functions let you trigger data workflows, perform ETL tasks, and react to real-time data changes.
Using container registries - Visual Studio Code
Users can connect to Docker registries from the following sources: Before you can deploy a Docker image, the image must be uploaded to a container registry. The image can be uploaded to Docker Hub, Azure Container Registry (ACR) or another registry.
Data Science in Visual Studio Code
Use Jupyter Notebooks and the Interactive Window to start analyzing and visualizing your data in minutes! Power your Python coding experience with IntelliSense support and build, train, and deploy machine learning models to the cloud or the edge with Azure Machine Learning service.
Azure Machine Learning in VS Code - Visual Studio Code
In Azure Machine Learning, you can use popular frameworks for training machine learning models such as scikit-learn, PyTorch, TensorFlow, and many more. The extension makes it easy to submit and track the lifecycle of those models.