Secure ML with Secure Software Dependencies
Engineers from Chainguard and Outerbunds discuss their common philosophy around security and machine learning workflows.
Seamless Data and ML Pipelines with Airflow and Metaflow
Learn how to develop ML, AI, and data science with Metaflow, deploy alongside existing data pipelines on Airflow.
Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs
Learn how to use Retrieval Augmented Generation to control hallucinations and get more relevant responses from LLMs.
New: Scale ML/AI Smoothly with @checkpoint, @model, and Flexible Compute Infrastructure
Today’s launches focus on scalable ML and AI: Thanks to new decorators, you can finetune foundation models, train ML models, leverage distributed training, and use Slurm without headaches
Develop and Train Large Models Cost-efficiently with Metaflow and AWS Trainium
The latest version of Metaflow includes support for AWS Trainium hardware accelerators which allow you to train large models cost-efficiently on AWS.
New in Metaflow: The Long-Awaited @pypi Decorator
You can now install dependencies from PyPI as well as Conda in your Metaflow steps.
Event: How to Build a Full-Stack Recommender System
Join Jacopo Tagliabue and Hugo Bowne-Anderson in a live code along session to dive into how to build a production-grade recommender system.
Build Reproducible and Scalable Computational Biology Systems
This post introduces high-level trends at the intersection of biology and AI, discusses new (and old) technical challenges in building reproducible and scalable systems for AI-driven computational biology, and how frameworks like Metaflow can help address them.