Model Observability

Datacast Episode 131: Data Infrastructure for Consumer Platforms, Algorithmic Governance, and Responsible AI with Krishna Gade

Datacast Episode 131: Data Infrastructure for Consumer Platforms, Algorithmic Governance, and Responsible AI with Krishna Gade

Krishna Gade is the founder and CEO of Fiddler AI, an AI Observability startup that helps AI-forward organizations build trusted AI solutions and connect model outcomes to business KPIs. Fiddler addresses problems in model monitoring, explainability, analytics, and fairness.

An entrepreneur and engineering leader with strong technical experience in creating scalable platforms and delightful products, Krishna previously held senior engineering leadership roles at Facebook, Pinterest, Twitter, and Microsoft. At Facebook, Krishna led the News Feed Ranking Platform that created the infrastructure for ranking content and powered use-cases like Facebook Stories and recommendations like People You May Know, Groups You Should Join, etc. Krishna’s team built Facebook’s explainability features like ‘Why am I seeing this?’ which helped bring much-needed algorithmic transparency and, thereby, accountability to the News Feed for both internal and external users.

What I Learned From Arize:Observe 2022

What I Learned From Arize:Observe 2022

Last month, I had the opportunity to speak at Arize:Observe, the first conference dedicated solely to ML observability from both a business and technical perspective. More than a mere user conference, Arize:Observe features presentations and panels from industry thought leaders and ML teams across sectors. Designed to tackle both the basics and most challenging questions and use cases, the conference has sessions about performance monitoring and troubleshooting, data quality and drift monitoring and troubleshooting, ML observability in the world of unstructured data, explainability, business impact analysis, operationalizing ethical AI, and more.

In this blog recap, I will dissect content from the summit’s most insightful technical talks, covering a wide range of topics from scaling real-time ML and best practices of effective ML teams to challenges in monitoring production ML pipelines and redesigning ML platform.

Datacast Episode 89: Observable, Robust, and Responsible AI with Alessya Visnjic

Datacast Episode 89: Observable, Robust, and Responsible AI with Alessya Visnjic

Alessya Visnjic is the CEO and co-founder of WhyLabs, the AI Observability company on a mission to build the interface between AI and human operators. Prior to WhyLabs, Alessya was a CTO-in-residence at the Allen Institute for AI (AI2), where she evaluated the commercial potential for the latest advancements in AI research.

Earlier in her career, Alessya spent nine years at Amazon, leading Machine Learning adoption and tooling efforts. She was a founding member of Amazon’s first ML research center in Berlin, Germany. Alessya is also the founder of Rsqrd AI, a global community of 1,000+ AI practitioners who are committed to making AI technology Robust & Responsible.

What I Learned From Attending Tecton apply(meetup) 2021

What I Learned From Attending Tecton apply(meetup) 2021

Last month, I attended apply(), Tecton’s follow-up virtual event of their ML data engineering conference series. I’ve previously written a recap of their inaugural event, a whirlwind tour of wide-ranging topics such as feature stores, ML platforms, and research on data engineering. In this shorter post, I would like to share content from the main talks and lightning talks presented at the community meetup. Topics include ML systems research, ML observability, streaming architecture, and more.

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Aparna Dhinakaran is the Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michelangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.