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.
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!
Last week, I attended Comet ML’s Convergence virtual event. The event features presentations from data science and machine learning experts, who shared their best practices and insights on developing and implementing enterprise ML strategies. There were talks discussing emerging tools, approaches, and workflows that can help you effectively manage an ML project from start to finish.
In this blog recap, I will dissect content from the event’s technical talks, covering a wide range of topics from testing models in production and data quality assessment to operational ML and minimum viable model.
Last month, I attended another apply(meetup), Tecton’s follow-up virtual event of their ML data engineering conference series. For context, I have written recaps for both of their 2021 events, including the inaugural conference and the follow-up meetup. The content below covers my learnings, ranging from model calibration and ranking systems to real-time analytics and online feature stores.
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.
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.
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!
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.
Jim Dowling is the CEO of Logical Clocks AB, an Associate Professor at KTH Royal Institute of Technology, and a Senior Researcher at SICS RISE in Stockholm. His research concentrates on building systems support for machine learning at scale. He is the lead architect of Hops Hadoop, the world's fastest and most scalable Hadoop distribution and only Hadoop platform with support for GPUs as a resource. He is also a regular speaker at Big Data and AI industry conferences.
