Common challenges in data science
Data science and machine learning teams are at the cutting edge of business innovation, bringing together insight and learning from data sets near and far. They must be cross-functional, agile, and iterative as they work with business and IT teams to unlock the value hidden in their organization’s data.
Data science teams need to:
- Collaborate both inside and outside their teams, and often inside and outside their organization
- Plan and manage projects and sprints, with tools flexible enough to support scrum, kanban, and more
- Version control everything: manage and track different versions of files, models, test cases, data sets
- Automate key workflow steps, that are otherwise slow and subject to manual errors
- Streamline testing and validation of work, making it much faster and more repeatable
- Simplify infrastructure management and often across multiple cloud providers
