We have come a long way since the publication of "Hidden Technical Debt in Machine Learning Systems" was published almost a decade ago. ML Ops has transformed how data science work is delivered, managed, and monitored. Great?

Maybe. In this discussion we cover what is still one of the most glaring gaps in the AI/ML field. Disagreement is accepted and encouraged.

Audio from our LinkedIn Live Event!

Organizational structure, team, and culture are critical components to repeatedly and consistently delivering innovation, business results, and absorbing innovative techniques from outside the org walls. AI is rightfully getting the lion’s share of attention, but to make purposed and impactful use, and to have it generate value, many teams across many people and many business units need to align on a baseline operating model. Without this, work efforts, collaboration, and implementations will continue to have marginal gains, if any.And the future will be left to the companies that have or will figure out operating models for innovation and AI.

Engineers are famous for building the amazing, and for wasting time on pet projects, dead ends, and losing track of the customer and real problems. How do you balance creativity and solving real problems? Jodi Blomberg, VP of Data Science at Cox Automotive, has sound, entertaining, and insightful advice!

Digital transformation has been around for a while, but have we succeeded at it? AI, for better and worse, is pushing change in organizations. One of the positives is that AI is creating urgency for organizations to do the things they have deprioritized for a long time: data management, governance, or in this case, digital transformation. Now that this is come back into focus, what are the foundations of effective change?

At the end of a spectacular achievement, the journey there can sometimes seem obvious, but we all know clarity at the start is frequently missing. Nancy Tickle was at the center of Chesterfield County's pioneering transformation in using data to forecast micro-level growth, achieving something no other city or county had done before. In this episode, Nancy offers a detailed account on how Chesterfield achieved remarkable results.

Software is eating the world (or used to, that was soooo 2011). AI is eating the world now at a blistering pace, and while a lot of it feels like hyperbole, and in some cases faked gains (Devin), many initiatives in AI & data are very real, and organizations are adopting and adapting their cultures to include these gains and take advantage of what they enable.

Within this astounding pace of change, we have lots of anxiety on learning and adapting. How do I stay current? Is RAG even going to be a thing a year from now?

Want some AI with your coffee? It seems anywhere you turn, AI is infused into every interaction, decision, and experience, so it makes sense that your organization should upgrade (or create) your AO strategy. But how? CDO Peggy Tsai offers incredibly practical advice on AI Strategy for today's Chief Data Officers.

"What's in a name?" Shakespeare's romantic notion that naming something is irrelevant may work in purposes of his plays, but for the data world, naming, and conformance on the meaning of names, is critical. Amit Pahwa has spent a good portion of his career dedicated to making our data lives easier, all by focusing on that most basic of tasks: naming things.