Arize AI

Drift Happens, featuring Elizabeth Hutton from Cisco
2022-04-14 13:00 - 13:30 (America/Los_Angeles) | https://arize.zoom.us/j/82831610497
Join us Thursday April 14th, 4pm ET/ 1pm PT for Drift Happens, Arize’s weekly video chat where our host Amber Roberts will discuss ML use cases, best practices in model development and model deployment, as well as troubleshooting models in production with industry’s top MLEs and Data Scientists.

This week we are joined by Elizabeth Hutton, Machine Learning Engineer at Cisco. Elizabeth has a background in computational math, cognitive science and NLP and is currently responsible for ML experimentation, model development, evaluation, and serving at Cisco. As the lead MLE on the Webex Contact Center AI Team, she is creating the first in-house AI solutions from R&D to production.

Drift Happens
2022-04-14 08:00 - 17:00 | Virtual, United States

Data Council
2022-03-23 08:00 - 2022-03-24 17:00 | Austin, Texas, Austin, TX, United States

Platform Demo with Gabe
2022-02-23 08:00 - 17:00 | Virtual, United States

World AI Conference - Cannes
2022-02-10 08:00 - 2022-02-12 17:00 | Cannes, France & Virtual

See It In Action: Arize Platform Demo, Live Q&A
2022-01-22 08:00 - 17:00 | Virtual, United States

Best Practices for ML Observability in Lending & Insurance
2021-12-08 08:00 - 17:00 | Virtual, United States

Best Practices in ML Observability for Monitoring, Mitigating and Preventing Fraud
2021-11-18 08:00 - 17:00 | Virtual, United States

ODSC West
2021-11-16 08:00 - 2021-11-18 17:00 | San Francisco, CA, San Francisco, CA, United States

TMLS
2021-11-15 08:00 - 2021-11-16 17:00 | Virtual, United States

The Rise of ML Observability
2021-10-12 08:00 - 17:00 | Virtual, United States
On-Demand
Detecting, diagnosing and resolving ML model performance can be difficult for even the most sophisticated ML engineers. Join Arize co-founders Aparna Dhinakaran and Jason Lopatecki as they reflect on the evolution of ML observability since pioneering the space over one year ago and demo the Arize AI platform publicly for the first time.
In this session, we will explore:
The challenges of productionalizing ML
Why an evaluation store is becoming a critical piece of the ML infrastructure stack
The four pillars of ML observability and how to tackle each: drift, performance analysis, data quality and explainability

ML Observability 101: How To Make Your Models Work IRL
2021-09-16 08:00 - 17:00 | Virtual, United States
