How do you measure the impact of data science? In this fireside chat, we’ll discuss a new way to frame and benchmark the ROI of your data science team’s work: Model Velocity.
Getting one model into production isn’t going to impact your business. But delivering many models – and constantly measuring, managing and improving them once they’re in production – that’s the key to success with data science. Model-driven businesses win because their CDOs create a flywheel for data science products that improve efficiencies and unlock breakthrough innovations.
These CDOs focus on weaving ML models throughout their entire business. They build out an MLOps strategy with Model Velocity in mind. They deliver on their strategy by setting meaningful benchmarks and continuously improving the performance of their data science teams over time. They establish a culture, processes, and systems that focus on rapid iteration throughout the data science lifecycle.
Watch this session to hear industry-leading CDOs discuss:
- Where they’re seeing bottlenecks or breakdowns occur in the data science lifecycle.
- How they’re thinking about building out an MLOps competency.
- How they’re measuring the performance of data science products today, and how they think about Model Velocity in their business.
- What key investments they’re making in 2021 surrounding data science, and why.
- Josh Poduska, Chief Data Scientist, Domino Data Lab (moderator)
- Meenakshi Thanikachalam, Head of Data Strategy, Architecture, Analytics, Ally Financial
- Sean Otto, Director of Analytics, AES