Name: Fabio M. Graetz, Ph.D.
Type: User
Company: Recogni
Bio: Technical Steering Committee @flyteorg. Lead MLOps engineer @ Recogni. Prior: MLOps Lead and ML engineer at Merantix.
Ph.D. in theoretical astrophysics
Location: Berlin, Germany
Blog: https://medium.com/@fabiograetz
Fabio M. Graetz, Ph.D.'s Projects
Training algorithm design and data structures
Creates bifurcation diagrams of real discrete one dimensional chaotic maps
Source files for the website
The 3rd edition of course.fast.ai
Cracking the Coding Interview 6th Ed. C++ Solutions
Tensorflow implementation of Deepminds dqn with double dueling networks
DeViSE model (zero-shot learning) trained on ImageNet and deployed on AWS using Docker
My emacs setup
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Fastai: Deep Learning from the foundations
Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale. It has been battle-tested at Lyft, Spotify, Freenome, and others and is truly open-source.
Control Plane for Flyte. Flyteadmin is a gRPC + REST Service written in golang and uses a RDBMs to store meta information and management information for Flyte Tasks and Workflows.
Specification of the IR for Flyte workflows and tasks. Also Interfaces for all backend services. https://docs.flyte.org/projects/flyteidl/en/stable/
Extensible Python SDK for developing Flyte tasks and workflows. Simple to get started and learn and highly extensible.
Flyte Backend Plugins contributed by the Flyte community.
FlytePropeller is a Kubernetes native operator, that executes Flyte Workflows and Tasks. It has its own kubectl-flyte CLI to interact and is extensible using the flyteplugins/pluginmachinery interface
Flyte User-Guides and Tutorials - https://flytecookbook.readthedocs.io
Hydra is a framework for elegantly configuring complex applications
Connect, secure, control, and observe services.
Notes and homework assignments to the course "Intro to Parallel Programming" by Nvidia on Udacity
Open source platform for the machine learning lifecycle
Code of the official webpage of onnx