AGENDA

In the last five years, hundreds of open-source projects have gone from academic research to foundational technologies. Many of these offer processing, storage, and analysis that simply wasn't possible before—and in so doing, have made existing approaches costly, clumsy, and obsolete. Learn what's new, and what's a liability, with a complete overview of data and machine learning platforms.

Tomorrow's tech teams look very different. From human-in-the-loop computing, to LLM-generated code, to automation, you'll need to hire, retain, upskill—and yes, replace—much of your organization. Before you make critical decisions, learn from experts in law, technology, and HR about the chimeral future of data-driven, AI-assisted employment.

Data science promises huge business advantages, with data-driven businesses outpacing their competitors. But turning those promises into reality is hard, requiring a strategy you can implement, alongside sweeping changes in how your organization operates. Learn how to turn the incredible possibilities of data and AI into concrete business outcomes, with real-world lessons you can put to work immediately.

Every new technology presents an untested "attack surface" for hackers. From data poisoning to adversarial prompt engineering to breaches, bad data engineering can put your customers, your platforms, and your organization at risk. Tackle the hard questions of information security, governance, and resiliency across every facet of your data strategy.

There are real first-mover advantages for early adopters of data science and AI. But there are also plenty of obstacles—some real, some perceived. We'll separate fact from fears and show digital leaders how to navigate the choppy waters of regulation, governance, compliance and ethics, building buy-in and support for pilot programs while crafting a data strategy the whole business can get behind.

Data-driven organizations are more competitive, outmaneuvering those who shoot from the hip. And analytics is how they aim. From statistical tools for classifying the past and predicting the future, to compelling visualizations and data storytelling, the field of Business Intelligence has flourished in the Big Data era. Now, machine learning and AI are upending the entire field, putting unstructured data and qualitative information in the grasp of analysts and decision-makers.

Nobody manages servers by hand anymore. Instead, we've blended software engineering with operational control, using scripts and automation to scale infrastructure and deploy applications automatically. The fields of DevOps and MLOps are among today's most sought-after careers. Learn how to wrangle complex virtual infrastructure and real-time data pipelines.

With Generative AI, we've created an entirely new AI stack. Generative tools need completely new processes for creating, testing, and iterating. AI and ML require a complete rethinking of how IT functions, from data pipelines to model training to experimentation. Organizations need a "two stack" architecture for deterministic and nondeterministic software in order to reap the benefits of modern advances in machine learning.