MLOps

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.

Datacast Episode 104: Streamlining Machine Learning In Production with Ran Romano

Datacast Episode 104: Streamlining Machine Learning In Production with Ran Romano

Ran Romano is the co-founder and VP of Engineering at Qwak, where he is focused on building the next-generation ML infrastructure for ML teams of various sizes. Before Qwak, Ran led the Data and ML engineering groups at Wix.com - where he built Wix's internal ML Platform. Previous to that, he was a Technical Product Manager at the Israeli Intelligence corps.

What I Learned From Tecton's apply() 2022 Conference

What I Learned From Tecton's apply() 2022 Conference

Back in May, I attended apply(), Tecton’s second annual virtual event for data and ML teams to discuss the practical data engineering challenges faced when building ML for the real world. There were talks on best practice development patterns, tools of choice, and emerging architectures to successfully build and manage production ML applications.

This long-form article dissects content from 14 sessions and lightning talks that I found most useful from attending apply(). These talks cover 3 major areas: industry trends, production use cases, and open-source libraries. Let’s dive in!

Datacast Episode 93: Open-Source Development, Human-Centric AI, and Modern ML Infrastructure with Ville Tuulos

Datacast Episode 93: Open-Source Development, Human-Centric AI, and Modern ML Infrastructure with Ville Tuulos

Ville Tuulos has been developing tools and infrastructure for data science and machine learning for over two decades. At Netflix, he led the machine learning infrastructure team. Currently, he is the CEO and co-founder of Outerbounds - where he’s building the modern, human-centric ML infrastructure stack, continuing the open-source product called Metaflow that he developed and managed during his Netflix days.

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.

Datacast Episode 84: Business Development and Customer Success for Emerging Technologies with Taimur Rashid

Datacast Episode 84: Business Development and Customer Success for Emerging Technologies with Taimur Rashid

As Chief Business Development Officer, Taimur is responsible for developing emerging businesses at Redis and leading strategic business & corporate development. He is currently leading initiatives related to AI/ML.

Prior to Redis, Taimur led Worldwide Customer Success for Microsoft's Azure Data & AI. He jointly led the design, implementation, and landing of one of Microsoft's largest field transformations, which combined customer success, support engineering, and technical account management.

Before Microsoft, Taimur was the Managing Director for Amazon Web Services (AWS) Platform Technology and Applications - where he led business development from 2008 (near its inception) to 2018 when the business reached $25B in ARR. Taimur helped forge key partnerships and customers, including Airbnb, CapitalOne, Dropbox, Liberty Mutual, NASA JPL, Nasdaq, Netflix, Nintendo, Intuit, SAP, and Samsung.

Taimur grew up in three countries and lived in five states. Bellevue, WA is home for him, where he lives with his wife and three boys. Taimur enjoys cross-training, hiking, and biking. He is an avid reader of technology, business, and history. He enjoys art, music, coffee, and cooking on the weekends for his family.

What I Learned From The Future of Data-Centric AI 2021

What I Learned From The Future of Data-Centric AI 2021

Last September, I attended Snorkel AI’s The Future of Data-Centric AI. This summit connects experts on data-centric AI from academia, research, and industry to explore the shift from a model-centric practice to a data-centric approach to building AI. There were talks discussing the challenges, solutions, and ideas to make AI practical, both now and in the future.

In this blog recap, I will dissect content from the conference’s session talks, covering a wide range of topics from weak supervision and fine-grained error analysis to MLOps design principles and data-centric AI case studies.

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.

What I Learned From Attending MLOps World 2021

What I Learned From Attending MLOps World 2021

Two months ago, I attended the second edition of MLOps: Production and Engineering World, which is a multi-day virtual conference organized by the Toronto Machine Learning Society that explores the best practices, methodologies, and principles of effective MLOps. In this post, I would like to share content from the talks that I found most useful during this conference, broken down into Operational and Technical talks.