Conference

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!

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.

What I Learned From Convergence 2022

What I Learned From Convergence 2022

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.

What I Learned From Attending Tecton apply(meetup) 2022

What I Learned From Attending Tecton apply(meetup) 2022

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.

What I Learned From DataOps Unleashed 2022

What I Learned From DataOps Unleashed 2022

Earlier this month, I attended the second iteration of DataOps Unleashed, a great event that examines the emergence of DataOps, CloudOps, AIOps, and other professionals coming together to share the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

In this long-form blog recap, I will dissect content from the session talks that I found most useful from attending the summit. These talks are from DataOps professionals at leading organizations detailing how they establish data predictability, increase reliability, and reduce costs with their data pipelines. If interested, you should also check out my recap of DataOps Unleashed 2021 last year.

What I Learned From The Modern Data Stack Conference 2021

What I Learned From The Modern Data Stack Conference 2021

Back in September 2021, I attended the second annual Modern Data Stack Conference, Fivetran’s community-focused event that brings together hundreds of data analysts, data engineers, and data leaders to share the impact and experiences of next-generation analytics. The presenters shared the transformations they experienced with their analytics teams, the new insights and tooling they enabled, and the best practices they employ to drive insights across their organizations.

In this long-form blog recap, I will dissect content from 14 sessions that I found most useful from the conference. These talks are broken down into 4 categories tailored to 4 personas: data engineers, data analysts, product managers, and data team leads. Let’s dive in!

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 The Open Source Data Stack Conference 2021

What I Learned From The Open Source Data Stack Conference 2021

I attended the Open-Source Data Stack Conference in late September 2021, the first-ever conference dedicated to building a modern data stack using open-source data solutions. The emergence of the modern data stack has seen a rapid spike in the number of data tools an organization can use to drive better decision-making. Open-source software helps you control the end-to-end flow of customer data throughout your organization to guarantee data auditability, allow data governance, support consumer data privacy, and enable productive engineer workflows.

In this blog recap, I will dissect content from the conference’s session talks, each being a building block of the open-source data stack to demonstrate how teams can build a data stack that reflects their needs.

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.