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We’re excited to introduce Mikel King (aka Mikyo), our newest Arizer! Mikyo will be joining our rockstar engineering team.

Mikyo was most recently a part of the founding team at Engine ML where he worked on distributed training infrastructure for deep learning. Before that, Mikyo worked at Apple Inc. as a Senior Engineer.

Mikyo graduated summa cum laude from the University of Southern California with a degree in Computer Engineering and Computer Science. He also has a partially completed graduate degree in HCI from CU Boulder.

Arize AI’s mission to hold AI accountable resonates strongly with Mikyo. …


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Chris Walter with a coffee in hand

We’re excited to introduce Christopher Walter, our newest Arizer! Chris will be joining our amazing Frontend Engineering team.

Chris was most recently at Facebook and Instagram where he was a Frontend Engineer working on Instagram shopping, ads, and various internal projects.

Chris holds a bachelor’s degree from UC Berkeley in Computer Science and Economics. Chris fell in love with machine learning through his econometrics background, which provided him new ways of performing insightful in depth analysis. Alongside Chris’ love for ML, he’s simply excited to build Arize from the ground up. …


Arize and Paperspace are pleased to announce a partnership available to Paperspace platform customers. A simple pre-tested integration that is easy to set up, is now available to Paperspace users.

Paperspace customers will have priority access to the Arize platform available for model monitoring, troubleshooting and explainability. The integration allows, with a few lines of code, simple integration, the ability to monitor data drift and model drift, and troubleshoot those problems in a purpose built platform designed for ML Observability.

Why Observability

The difference between research environments and production can cause large issues for models deployed in the real world. The inputs models see, the degradation over time and the performance problems that arise, can be painful to troubleshoot. Observability helps teams go from research to production maintaining the results delivered, and helps teams troubleshoot problems quickly without eating up Data Science cycles. …


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We’re excited to introduce Nate Mar, our newest Arizer! Nate will be joining our rockstar engineering team.

Nate was most recently at PagerDuty where he worked on ML infrastructure, ML and incident management products, and alert notification pipelines.

ML fairness and transparency is near and dear to Nate’s work. In his own words:

“I’m excited about Arize’s mission to help ML engineers and data scientists better and more quickly understand how their models are performing in production. This strongly resonates with me having experienced some of the complex challenges of managing models in production myself. …


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Arize AI, the first-to-market ML Observability Platform, is excited to announce that it has been selected as a 2020 TiE50 Winner in the prestigious TiE50 Awards Program. This ten year old awards competition is a program of TiEcon, the world’s largest conference for tech entrepreneurs. Arize AI was recognized for its innovative platform to troubleshoot, monitor, and explain AI.

“Arize AI is the first go-to-market ML Observability platform. We are the only platform to gather actuals and capture true model performance. As businesses deploy more models into production and these models get more complex, model observability is key to making models successful.” …


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We’re excited to introduce Manisha Sharma, the newest Arizer! Manisha will be joining Arize AI’s Frontend Engineering team.

Manisha was most recently a Frontend Engineer at Slack where she worked on user growth and onboarding experiences. Before that, she worked on data visualization and design systems at Pandora Music. She holds a bachelor’s degree in Cognitive Science from UC Berkeley.

Manisha immigrated to the Bay Area from Fiji when she was 7 years old. Growing up, she attended schools that did not have developed technology tracks. It is for this reason that Manisha has developed such a strong passion for inclusiveness, accessibility and transparency in the tech field. …


Model Building — The Second Stage of ML Workflow

Originally published in Towards Data Science

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ML Infrastructure Platforms Diagram by Author

Artificial Intelligence (AI) and Machine Learning (ML) are being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement of leveraging AI in their business. Understanding the various platforms and offerings can be a challenge. The ML Infrastructure space is crowded, confusing, and complex. There are a number of platforms and tools spanning a variety of functions across the model building workflow.

To understand the ecosystem, we broadly break up the machine learning workflow into three stages — data preparation, model building, and production. Understanding what the goals and challenges of each stage of the workflow can help make an informed decision on what ML Infrastructure platforms out there are best suited for your business’s needs. …


Data Preparation — The First Stage of the Machine Learning Workflow

Originally published on Towards Data Science

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ML Infrastructure Platforms Diagram by Author

Artificial Intelligence (AI) and Machine Learning (ML) are being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement of leveraging AI in their business. Understanding the various platforms and offerings can be a challenge. The ML Infrastructure space is crowded, confusing, and complex. There are a number of platforms and tools spanning a variety of functions across the model building workflow.


When the past is no longer relevant to the present, how can we predict the future?

Originally published on Towards Data Science

How to build Resilience in Production AI/ML during Outlier Events & Extreme Environments

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By Gala Che on Shutterstock

Challenges of Extreme Environments

Coronavirus is the black swan of 2020. Not only is the initial on-set of the virus an unexpected extreme outlier event, the human reaction to try to contain the virus is creating massive ripples through systems that run the world — health, business, finance, gig-economy, credit, commerce, auto-traffic and travel to name a few.

Black Swan events pose particular challenges for machine learning (ML) models. ML models are trained on previously seen observations to predict future scenarios. However, today these models are seeing events that are drastically different from what they were ever trained on. Many businesses (especially in credit and finance) have 100’s-1000’s of live production models running in their organization, making incorrect decisions on data that affect their business outcomes tomorrow. …


Why is it impossible to understand what AI companies really do?

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Traffic Chaos By sladkozaponi via Shutterstock

The State of AI in Businesses

Over the last several years, there’s been a rush to find out how to integrate AI into businesses, and it’s no secret that doing so could offer huge comparative advantages. But for all the hype, AI in businesses is still very much in the early phase.

Our team hails from Uber, Google, Facebook and Adobe where we’ve seen both the positives and challenges of deploying AI across business lines. Most companies don’t have the same resources to build in-house tools, deeply measure results and fund extensive research. …

About

Arize AI

Arize AI is the watcher, troubleshooter and the guardrail on deployed AI.

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