Episode
Accelerate Development And Delivery Of Your Machine Learning Projects With A Comprehensive Feature Platform
- Podcast
- AI Engineering Podcast
- Published
- Aug 6, 2022
- Duration seconds
- 3038
- Processing state
failed
Actions
POST https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/accelerate-development-and-delivery-of-your-machine-learning-projects-with-a-comprehensive-feature-platform/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/ai-engineering-podcast/accelerate-development-and-delivery-of-your-machine-learning-projects-with-a-comprehensive-feature-platform.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
Summary In order for a machine learning model to build connections and context across the data that is fed into it the raw data needs to be engineered into semantic features. This is a process that can be tedious and full of toil, requiring constant upkeep and often leading to rework across projects and teams. In order to reduce the amount of wasted effort and speed up experimentation and training iterations a new generation of services are being developed. Tecton first built a feature store to serve as a central repository of engineered features and keep them up to date for training and inference. Since then they have expanded the set of tools and services to be a full-fledged feature platform. In this episode Kevin Stumpf explains the different capabilities and activities related to features that are necessary to maintain velocity in your machine learning projects. 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! Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to ma…