If you’re ready to explore the capabilities of data science platforms, there are some key capabilities to consider:
Choose a project-based UI that encourages collaboration. The platform should empower people to work together on a model, from conception to final development. It should give each team member self-service access to data and resources.
Prioritize integration and flexibility. Make sure the platform includes support for the latest open source tools, common version-control providers, such as GitHub, GitLab, and Bitbucket, and tight integration with other resources.
Include enterprise-grade capabilities. Ensure the platform can scale with your business as your team grows. The platform should be highly available, have robust access controls, and support a large number of concurrent users.
Make data science more self-service. Look for a platform that takes the burden off of IT and engineering, and makes it easy for data scientists to spin up environments instantly, track all of their work, and easily deploy models into production.
Your organization could be ready for a data science platform, if you’ve noticed that:
IDSA courses are designed to prepare students for real commercial work in the area of data science, AI and big data.
IDSA trainers are actively working in the industry and will teach you how to practice data science and advanced analytics.
The member network offers support and guidance from mentors and direct connections to the industry. You will meet employers in-person at IDSA events.