What we’re about
Welcome data practitioners and open source aficianados — of all backgrounds and experience levels!
We are a group of data engineers, feature engineers and data scientists who want to build high performance, resilient predictive models.
Feature engineering is a critical part of the machine learning life cycle, but it is time consuming and complicated. In many cases more of an art than a science, the quality of your feature engineering ultimately determines the performance of your models.
This group will be focused on all aspects of feature engineering including:
- Analyzing, cleaning and preparing base data to construct features:
- Feature construction via transformations
- Understanding and addressing a multitude of challenges that cause non-optimal feature construction
- Building and Operationalizing models using the right features
We’re an online and in-person data-enthusiast group hosting live and online speaking events on a range of topics related to:
- Data Engineering
- Open Source
- Machine Learning
- Feature Engineering
- Predictive Modeling
Join us in person if possible or on one of our live streams!
Upcoming events
8
- Network event
•OnlineNot Just for Engineers: What Agentic Data Management Unlocks
Online6 attendees from 11 groupsMost conversations about automating data workflows center on the engineering stack—but that’s only part of the story. The real payoff comes downstream, where data scientists, analysts, and business leaders need fast, reliable access to data to make informed decisions. In this session, the Matillion team will demonstrate how an agentic system like Maia shifts repetitive data tasks into autonomous execution, freeing up teams to focus on high-impact analysis and modeling.
We’ll walk through how this shift reduces backlog, accelerates time-to-insight, and increases trust in the data that drives both machine learning pipelines and executive dashboards. Through real-world examples, attendees will see how agentic workflows empower the entire data organization—not just engineering.
Key Takeaways:
1️⃣ Workload Lift for Analysts & Scientists: How automation reduces manual prep and speeds up modeling cycles
2️⃣ From Backlog to Business Value: How agentic orchestration gets more data into production, faster
3️⃣ Organizational Trust in Data: Why agentic systems improve confidence in the data used for decision-making
4️⃣ Cross-Functional Impact: How these changes improve collaboration between data engineering, analytics, and leadership teams
PANELISTS TO BE ANNOUNCED SOON
Register here - Network event
•OnlineFrom Black Box to Glass Box: Observability for Scalable AI Systems
Online28 attendees from 11 groupsAs AI systems continue to scale, observability has become an essential component for success. This webinar explores two key dimensions: data observability, which focuses on monitoring and improving the quality of data that drives AI systems, and AI observability, which provides transparency and accountability for model performance and decision-making.
Learn practical strategies for building "glass box" AI systems where insights are clear, issues are easy to diagnose, and scalability is seamless.
Key Takeaways:
- Understand Data Observability: Learn how to implement monitoring systems to ensure high-quality, reliable data pipelines for AI systems.
- Explore AI Observability: Discover tools and techniques for monitoring AI models, ensuring they are transparent, unbiased, and explainable.
- Bridge the Gap: Integrate data and AI observability practices to achieve robust, scalable, and trustworthy AI deployments.
Panelists to be announced soon
Register Here1 attendee from this group - Network event
•OnlineNot Your Typical AI Webinar: 4 Practical Tips for Real Results
Online1 attendee from 11 groupsTired of AI sessions full of hype and light on substance? This webinar skips the buzzwords and gets straight to what works. Hear from a seasoned panel of analysts, data scientists, and business leaders as they share field-tested strategies for making AI actually deliver.
You’ll walk away with practical questions to ask before your next AI project launches, learn how to connect your data initiatives to real ROI, and avoid common traps that derail transformation efforts. With decades of combined experience helping organizations navigate the messiness of modern data and AI, this panel is here to cut through the noise.
Panelists:
📢 Stephen Assink, Technical Business Analyst
📢 Shaylee Davis, Data Scientist
📢 Josh Fairchild, Senior Technical Business Analyst
📢 Leah Severance, Director of Commercial Business Development
📢 Ramzi Ziade, Learning Engagement Manager
Key Takeaways:
1️⃣ Ask Smarter Questions: Learn the must-ask questions that separate successful AI projects from the ones that stall out.
2️⃣ Get ROI, Not Just Reports: Explore techniques to connect AI efforts to clear, measurable outcomes.
3️⃣ Bridge the Gaps: See how cross-functional collaboration between business, data, and learning teams unlocks value.
4️⃣Learn from the Field: Hear what’s actually working (and what’s not) from professionals who’ve been in the trenches.
Register Here - Network event
•OnlineBeyond the Prototype: What It Takes to Build Enterprise-Grade AI
Online12 attendees from 11 groupsThe AI landscape is cluttered with impressive demos and promising proofs of concept—but turning those early wins into real, scalable impact is a different challenge entirely. This webinar dives deep into what it actually takes to evolve from experimentation to production in enterprise AI.
Join a panel of experts who have built and deployed AI at scale to explore the operational, architectural, and organizational requirements that separate enterprise-ready AI from pilot projects that never leave the lab. We'll cover how to navigate infrastructure decisions, design for governance and observability, and build systems that are robust, compliant, and built to last.
Key Takeaways:
1️⃣ From Idea to Impact: What separates successful enterprise AI deployments from stalled prototypes.
2️⃣ Architecting for Scale: Best practices for building AI pipelines that are modular, maintainable, and audit-ready.
3️⃣ Trust and Governance: How to bake in model observability, compliance, and responsible AI from day one.
4️⃣Collaboration Across Functions: Why cross-team alignment (ML, IT, data, product) is essential—and how to make it work in practice.
5️⃣ Lessons from the Field: Real-world insights from leaders who’ve scaled AI across industries.
Panelists to be announced soon.
Register here1 attendee from this group
Past events
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