Tools for setting up and running pipelines in a Data Analytics and Production System (DAPS).
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Updated
Sep 21, 2022 - Python
Tools for setting up and running pipelines in a Data Analytics and Production System (DAPS).
Zephyr is a command-line utility that provides project and component scaffolding to build modular pipelines.
Using Metaflow for training a DL model with Tensorflow.
A recommender system for book recommendations developed within a production-ready workflow. This project utilizes Metaflow, AWS, and the Surprise library.
MalaysianPayGap LLM using LocalGPT
Sentiment Analysis pipeline with SKLearn, Metaflow, AWS and GitHub CI
Parallelizing ImageNet Training with Metaflow On Kubernetes
Leverage Metaflow, PyTorch, AWS S3, Elasticsearch, FastAPI and Docker to create a production-ready facial recognition solution. It demonstrates the practical use of deep metric learning to recognize previously unseen faces without prior training.
Metaflow On Kubernetes
Get Yu-Gi-Oh! card recommendations by the magic of machine learning
Experiments in dispatching Metaflow flows to Flyte.
Fully functional Metaflow metadata service, UI and datastore deployment with docker and docker-compose.
A pipeline built on MetaFlow for training Fashion MNIST dataset using Pytorch, experiment tracking using MLFlow and model deployment using BentoML
Experimentation Of different deep learning models for classification of digits on a MNIST dataset using Metaflow
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