Arize AI

Community Paper Reading: VOYAGER
2023-06-14 10:15 - 11:00 (America/Los_Angeles) | Virtual, United States

TMLS
2023-06-14 08:00 - 17:00 (America/Los_Angeles) | Toronto, Canada, Toronto, Canada

Beyond the Hype: Putting LLMs and AI into production
2023-06-13 09:00 - 10:00 (America/Los_Angeles) | Virtual, United States

Community Paper Reading
2023-06-07 10:15 - 11:00, 2023-05-31 10:15 - 11:00, 2023-05-24 10:15 - 11:00 (America/Los_Angeles) | Virtual, United States

Workshop Series Part 4: Fundamentals of Dynamic Monitoring and Performance Insights
2023-06-07 09:30 - 10:00 (America/Los_Angeles) | Virtual, United States

Fully Connected
2023-06-07 08:00 - 17:00 (America/Los_Angeles) | San Francisco, United States

Workshop: Fine-tune and Analyze LLMs
2023-06-01 17:30 - 20:00 (America/Los_Angeles) | San Francisco, United States

Workshop Series Part 3: Zero To Hero: High-Dimensional Data Visualization
2023-05-31 09:30 - 10:00 (America/Los_Angeles) | Virtual, United States

Apply (risk)
2023-05-30 09:30 - 14:00 (America/Los_Angeles) | Virtual, United States

Workshop Series Part 2: Real-Time Observability In ‘The Age of AI’
2023-05-24 09:30 - 10:00 (America/Los_Angeles) | Virtual, United States

[Workshop] ML Observability and Active Learning for Image Classification with Phoenix
2023-05-18 11:00 - 23:30 (America/Los_Angeles) | Virtual, United States

Workshop Series Part 1: Identifying Recommendation Bias In Ranking Models
2023-05-17 09:30 - 10:00 (America/Los_Angeles) | Virtual, United States

ODSC East
2023-05-11 11:00 - 12:00 (America/New_York) | Boston, MA, United States

Arize:Observe 2023
2023-04-25 - 2023-04-26

NVIDIA CTA: How to Develop AI Workflows and MLOps Infrastructure for Scale
2023-03-21 13:00 - 14:00 (America/Los_Angeles) | Virtual, United States
This panel will discuss and recommend building an MLOps solution for AI workflows and enterprise applications. The need for continuous delivery and automated deployment of AI workloads is a core focus of every organization.

Manish Harsh, Global DevRel, MLOps Integrations and Partners, NVIDIA
Yaron Haviv, Co-Founder and CTO, Iguazio
Aparna Dhinakaran, Co-founder & Chief Product Officer, Arize AI
Shelbee Eigenbrode, Principal ML Specialist Solutions Architect, Amazon Web Services (AWS)

Data Visualization for ML Observability in 3 Easy Steps
2023-03-08 10:00 - 10:30 (America/Los_Angeles) | Virtual, United States

Advanced Metrics Workshop Series: Model Drift Metrics in Production
2023-02-23 09:00 - 09:30 (America/Los_Angeles) | Virtual, United States

AI at Scale
2023-02-23 - 2023-02-24 | Virtual, United States

Automatically Surface Issues with Unstructured Data
2023-02-22 11:00 - 11:30 (America/Los_Angeles) | Virtual, United States

Advanced Metrics Workshop Series: ML Performance Metrics in Production
2023-02-16 09:00 - 09:30 (America/Los_Angeles) | Virtual, United States

Advanced Metrics Workshop Series: AI Fairness Metrics in Production
2023-02-09 09:00 - 09:30 (America/Los_Angeles) | Virtual, United States

Arize + Anyscale Webinar: Productionizing Machine Learning with Observability, Quality and Flexibility at Scale
2023-02-07 10:00 - 10:30 (America/Los_Angeles) | Virtual, United States

WaiSymposium & Virtual Career Fair 2023
2023-01-27 12:00 - 15:30 (America/New_York) | Virtual, United States

Improve Ranking Model Performance With ML Observability
2023-01-25 09:00 - 09:30 (America/Los_Angeles) | Virtual, United States

Keeping Your Model In Production
2022-12-06 07:30 - 08:30 (America/Los_Angeles) | Virtual, United States
So you have built and deployed your model into production – now what? Building a model and taking it from experimentation to production is hard — and keeping it there is even harder! How do you know your models are doing well? And how do you know when you need to retrain your model?
During this workshop, we’ll tackle these questions through the lens of a credit card churn model use case.
We’ll cover the following steps:

Ingest data from storage into Arize
Set up monitoring around drift, data quality and performance
Troubleshoot performance degradation and model drift
Implement fairness checks in production

By the end of this workshop, you’ll have the ability to know when your model is broken, how to quickly find the root cause, and when to retrain.

Pragmatic Applications: - Optimize Demand Forecasting
2022-11-30 08:00 - 17:00 (America/Los_Angeles) | Virtual, United States

Toronto Machine Learning Summit 2022
2022-11-28 - 2022-11-30 | Toronto, Canada, Toronto, Canada

Pragmatic Applications: NLP Classification
2022-11-16 09:00 - 09:30 (America/Los_Angeles) | Virtual, United States
From images and video to natural language and audio, unstructured data coupled with machine learning can unlock deeper AI potential and ROI for many organizations and use cases. Embeddings are the core of how deep learning models represent structures and are fundamental to how the next generation of ML models work.
Join this workshop to:

Troubleshoot a sentiment classification model in production
Learn about emerging techniques like UMAP to transform unstructured data into embeddings that can be more efficiently processed by ML models
Implement new technologies to monitor and improve models in production

Sign up now and try out these approaches and tools in a hands-on workshop!
