Practical feature engineering
February 11, 2020
Feature engineering is generally the section that gets left out of machine learning books, but it's also the most critical part in practice. Ted Dunning explores techniques, a few well known, but some rarely spoken of outside the institutional knowledge of top teams, including how to handle categorical inputs, natural language, transactions, and more in the context of machine learning.
Kubernetes day 3: The state of Kubernetes development tooling
January 28, 2020
Developers working with Kubernetes still wonder what the optimal development workflow looks like. Ellen Korbes explores the capabilities of the tooling available in the current landscape and sees if it can offer end-to-end workflows that perform effectively in the real world.
Machine learning vital signs: Metrics and monitoring of AI in production
January 27, 2020
Production artificial intelligence systems are interacting with the real world, and it's terrifying that oftentimes nobody has any idea how they're performing on live data. Donald Miner details why you should track your models in production over time, explains how you can implement proper logging and metrics for models, and details metrics you should probably be capturing.
Success at the Edge - Leveraging Open Source at the Edge as a Bridge to Critical Operations in Industry
January 6, 2020
In this talk, Richard and Bill present real-world edge solutions, exploring motivations, challenges and opportunities faced by manufacturing operations in extending visibility from the dark edge into …
Machine learning for personalization
December 30, 2019
For many years, the main goal of the Netflix recommendation system has been to get the right titles in front of each member at the right time. Tony Jebara details the approaches Netflix uses to recommend titles to users and discusses how the company is working on integrating causality and fairness into many of its machine learning and personalization systems.
Turn devices into data scientistsat the edge
December 28, 2019
Todays approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it allleaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head.
Online evaluation of machine learning models
December 22, 2019
Evaluating machine learning models is surprisingly hard, particularly because these systems interact in very subtle ways. Ted Dunning breaks the problem of evaluation apart into operational and function evaluation, demonstrating how to do each without unnecessary pain and suffering. Along the way, he shares exciting visualization techniques that will help make differences strikingly apparent.
Microservices architecture in the real world
December 16, 2019
Once you decide to adopt a microservices architecture, you'll face many more decisions and questions about routing, management, observability, developer experience, and more. Mason Jones shares approaches based on his real-world experiences making the shift to microservices.