As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them.
Learn how to modernize ITís approach to ensure your companyís data science teams perform their best, and maximize impact to the business. Some highlights include:
- Why data science should not be treated like engineering.
- How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle.
- Why agility and special hardware to support burst computing are so important to data science breakthroughs.
- How to support data scientistsí needs to experiment with new tools quickly.
- How to integrate new models into your existing systems to drive business impact.