Testable Technical Documentation with Notebooks, nbdev, and Docusaurus
Learn how you can streamline authoring, testing and maintenance of technical documentation with Jupyter Notebooks, nbdev and Docusaurus.
Machine Learning with the Modern Data Stack: A Case Study
Leveraging the Modern Data Stack for Machine Learning: how to combine DataOps and MLOps to produce business value.
Director of AI, Coveo
Head of DevRel, Outerbounds
5 Ways to Use the New Metaflow Tags
We are happy to announce support for mutable tags in Metaflow! Learn five key patterns for effective human-in-the-loop workflows which leverage the new feature.
Software Engineer, Outerbounds
Co-Founder, Outerbounds
Developing scalable feature engineering DAGs
Discover the power of DAGs to level up your data science projects with examples from feature transforms to workflow orchestration.
Data Scientist, Outerbounds
Data/ML Platform Lead, Stitch Fix
Introducing Metaflow Resources for Data Science and Engineering
A knowledge base of free, practical data science and machine learning materials, including how to set up a modern data science infrastructure.
The Modern Stack of ML Infrastructure
A discussion about the machine learning deployment stack and why data makes software different.
Co-Founder, Outerbounds
Head of DevRel, Outerbounds
Human-Centric Data Science on Kubernetes with Metaflow
Data scientists can now scale out their machine learning workflows to Kubernetes clusters using Metaflow and Argo, without needing to know the ins and outs of K8S.
Co-Founder, Outerbounds
Fireside chat #2: Teaching Practical Machine Learning
How to get started today with ML in production: tools, workflows, and mental models.
Head of DevRel, Outerbounds
Fireside chat #1: How to Produce Sustainable Business Value with Machine Learning
A fireside chat about product management for ML products across various industries.
Head of DevRel, Outerbounds
Excited to join Metaflow and Outerbounds
Hugo Bowne-Anderson discussed his journey to becoming Head of Developer Relations at Outerbounds.
Head of DevRel, Outerbounds
Data Scientists Don't Need to Know Kubernetes with Metaflow
With Metaflow, data scientists can leverage K8s clusters for their work. Metaflow presents a user-friendly UX to data scientists, while working nicely with production infrastructure.
Notebooks In Production With Metaflow
Learn how to use notebooks in production ML pipelines with a new Metaflow feature. This is model framework-agnostic and so will work with all types of ML models.
Head of Data Science, Outerbounds
Metaflow highlights of 2021
Developed internally at Netflix and open-sourced in 2019, Metaflow is now used to power machine learning in production by hundreds of companies.
Integrating Pythonic visual reports into ML pipelines
Introducing DAG cards for machine learning pipelines. These cards make it easy to attach custom visual reports in every workflow, without having to install any additional tooling.
Software Engineer, Outerbounds
Co-Founder, Outerbounds
MLOps vs DevOps: the Difference Data Makes
A discussion about the machine learning deployment stack and why data makes software different.
Head of DevRel, Outerbounds
Machine learning pipelines: from prototype to production
Build a simple production-ready MLOps pipeline using Metaflow, an OSS framework allowing data scientists to build production-ready machine learning workflows using a simple Python API, and Seldon.
Co-Founder, Outerbounds
CTO, Seldon