Steph Locke

Ten years ago, I began advocating for **DataOps**, a framework designed to improve collaboration, efficiency, and agility in data management. The industry was still grappling with fragmented workflows, slow delivery cycles, and a disconnect between data teams and business needs. Fast forward to today, and the landscape has transformed, but have we truly embraced the future of leveraging data at scale? This session will reflect on the evolution of DataOps, examining what’s changed, what challenges persist, and where we're headed next. **Key Takeaways:** ✅ The biggest wins and ongoing struggles in implementing DataOps over the last decade. ✅ Practical strategies for improving automation, governance, and data quality in modern workflows. ✅ How emerging trends like AI-driven automation and real-time analytics are reshaping the way we approach data management. ✅ Actionable insights on how data teams can stay agile and align better with business objectives. **Why Attend?** If you're a data professional, architect, or leader striving for operational excellence, this talk will equip you with the knowledge to future-proof your data strategies.

Azure Functions are great for a wide range of scenarios, including working with data on a transactional or event-driven basis. In this session, we'll look at how you can interact with Azure SQL, Cosmos DB, Event Hubs, and more so you can see how you can take a lightweight but code-first approach...

DataOps helps us add quality, speed, and automation into our work so that the traditional 80% time spent data wrangling is lower and the Business As Usual will be lower too.

Data science is a rapidly evolving skill set and keeping up to date with best practices for the workflow can be tough. In this one day course, we get hands-on with the latest tools to help us build reproducible, scalable models quickly and effectively. Starting with an overview of Azure ML Services and the latest advancements like R Support, we'll connect to common data sources, build our feature engineering pipelines, train models, and then deploy them. Throughout all of this Azure ML Services will be the foundation for our best practices workflow. Bring your laptop and your Python or R skills for a day of practical learning that you can implement immediately.

If AI is on the cards in your business you might need to make a recommendation as to whether your company should Buy or Build. Come to this session to work out the moving parts to turn "It Depends!" into an answer.