# Declarative Machine Learning For High Performance Deep Learning Models With Predibase

Page: https://stenobird.com/podcast/ai-engineering-podcast/declarative-machine-learning-for-high-performance-deep-learning-models-with-predibase
Text version: https://stenobird.com/podcast/ai-engineering-podcast/declarative-machine-learning-for-high-performance-deep-learning-models-with-predibase.md
Podcast: [AI Engineering Podcast](https://stenobird.com/podcast/ai-engineering-podcast)
Published: 2022-07-21T23:00:00+00:00
Episode link: https://www.aiengineeringpodcast.com/predibase-declarative-machine-learning-episode-4
Audio file: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638530538841635223b2917f8d-14b0-4a17-bae8-81f31f643cedv1.mp3
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JSON: https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/declarative-machine-learning-for-high-performance-deep-learning-models-with-predibase
Duration seconds: 3620

## Resource

Summary Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-trainin…

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