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OUR LATEST ARTICLES

How to extract knowledge from documents with Google PaLM 2 LLM

DevFest West Coast 2020

Video: Machine Learning Engineering with Tensorflow Extended

Video: How to Build a Reproducible ML Pipeline

Video: ML adventures with AutoML and TFHub

Video: TensorFlow.js — Superpowers that Scale in the Browser & Beyond

OUR LATEST RESOURCES

OUR LATEST ARTICLES

How to extract knowledge from documents with Google PaLM 2 LLM

PaLM 2 is Google's next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than previous state-of-the-art LLMs. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements. This article offers a quick and straightforward method for leveraging the PaLM 2 API to extract knowledge and ask questions from text.

DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

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