DVC
Tutorial: Scalable and Distributed ML Workflows with DVC and Ray on AWS (Part 2)
Need to setup DVC to work with Ray Cluster on AWS? This tutorial has you covered!
Tutorial: Scalable and Distributed ML Workflows with DVC and Ray (Part 1)
This tutorial introduces you to integrating DVC (Data Version Control) with Ray, turning them into your go-to toolkit for creating automated, scalable, and distributed ML pipelines.
Running DVC on a SLURM cluster
Learn how Exscientia uses DVC experiments on a cloud-deployed SLURM cluster to scale their ML experimentation.
Integrating DVC and Git LFS via libgit2 filters
Read about how we built a Python Git LFS client to support integrating
projects which use Git LFS into your DVC workflow.
Turn Your Favorite IDE into a Full Machine Learning Experimentation Platform
DVC extension enables you to run, track and manage ML experiments without leaving VS Code.
Leveraging LLMs in Chatbots: The DVC Approach
Read how DVC can optimize the development process for chatbots built on Large Language Models.
Fine-Tuning Large Language Models with a Production-Grade Pipeline
This post describes a production ML pipeline for fine-tuning large language models using DVC, SkyPilot, HuggingFace Transformers, and quantization techniques.
Automate model deployment to Amazon SageMaker with the DVC Model Registry
DVC provides a Git-based mechanism to automate model deployment from an intuitive web UI.
The DVC 3.0 Stack: Beyond the Command Line
DVC 3.0 introduces a stack of tools outside the command line to bring it closer to
where you work (in code, IDE, web) while also focusing on DVC fundamentals.
