PipelineAI
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Updated
Apr 17, 2024 - Jsonnet
PipelineAI
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
Developers helping developers. TFX-Addons is a collection of community projects to build new components, examples, libraries, and tools for TFX. The projects are organized under the auspices of the special interest group, SIG TFX-Addons. Join the group at http://goo.gle/tfx-addons-group
Build Low Code Automated Tensorflow explainable models in just 3 lines of code. Library created by: Hasan Rafiq - https://www.linkedin.com/in/sam04/
Assignments of "Machine Learning Engineering for Production (MLOps) Specialization" by Coursera (https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
Machine Learning Pipeline for Semantic Segmentation with TensorFlow Extended (TFX) and various GCP products
Kubeflow pipelines built on top of Tensorflow TFX library
End-to-end pipeline with TFX to train and deploy a BERT model for sentiment analysis.
Machine Learning with TensorFlow Extended (TFX) Pipelines
An example of TFX intended to work with Vertex AI in Google Cloud
NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
TangoFX Sessions is a plug and play platform for Internet calling. It not only makes video call over internet but also provides us the ability to use immensely interactive tools with our communication for example features such as creating drawing or writing code together while in a video call.
Building end-to-end production grade machine learning pipelines.
Code for creating end-end TFX production pipeline for GPT-2.
Using pachyderm as pipeline engine to serve "taxi chicago" machine learning models
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