Where I express personal thoughts in words
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.
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.
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.
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.
Nick Handel is Transform's CEO and Co-Founder. Before Transform, Nick was Head of Data at Branch International in the micro-lending space.
Before Branch, Nick held a variety of roles at Airbnb, both as a Data Scientist & Product Manager. He was on the Growth team that founded the Experiences product and the Data Platform team. His work includes launching Airbnb's ML platform, Zipline, building the company's data science team, helping with the company's initial international expansion, and leading the data science team that launched Airbnb's Trips product.
Before joining Airbnb, Nick was a research economist at BlackRock. He is an avid trail runner, climber, skier, and adventurer, much of the time with his dog Huckleberry.
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.
Last month, I attended two great summits organized by REWORK: The MLOps summit that discovers how to optimize the ML lifecycle & streamline ML pipeline for better production and the ML Fairness summit that discovers strategies to ensure ML models are accountable & fair to build secure & responsible AI. As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.
In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the summit. The post consists of 10 talks that range from automated data labeling and pipeline optimization, to model fairness and responsible AI at scale.
Last week, I attended apply(), Tecton’s first-ever conference that brought together industry thought leaders and practitioners from over 30 organizations to share and discuss ML data engineering’s current and future state. The complexity of ML data engineering is the most significant barrier between most data teams and transforming their applications and user experiences with operational ML.
In this long-form blog recap, I will dissect content from 23 sessions and lightning talks that I found most useful from attending apply(). These talks cover everything from the rise of feature stores and the evolution of MLOps, to novel techniques and scalable platform design. Let’s dive in!
A few weeks ago, I attended Transform, Scale AI’s first-ever conference that brought together an all-star line-up of the leading AI researchers and practitioners. The conference featured 19 sessions discussing the latest research breakthroughs and real-world impact across industries.
In this long-form blog recap, I will dissect content from the session talks that I found most useful from attending the conference. These talks cover everything from the future of ML frameworks and the importance of a data-centric mindset to AI applications at companies like Facebook and DoorDash. To be honest, the conference's quality was so amazing, and it’s hard to choose the talks to recap.
Willem Pienaar is the Engineering Lead at Tecton and the creator of Feast, a feature store for machine learning. Feast is an open-source project that Willem developed while leading the machine learning platform team at Gojek, the Indonesian ride-hailing startup. In a previous life, Willem founded and sold a networking startup and worked on industrial data systems.
