AI Engineering enables organizations to industrialize Artificial Intelligence (AI) to rapidly deliver business value. It is a discipline that encompasses a streamlined AI development lifecycle, including: DataOps, ModelOps, and DevOps, allowing for uninterrupted transitions for AI models across operational and maintenance environments.
The idea behind AI Engineering is to bring together the many different roles from Data Engineers, Data Scientists, Software Engineers, System Administrators, DevOps Engineers, Domain SME, Business Owners, Auditors to Customers involved when building AI. Many different roles or personas equates to many different technologies being used across the enterprise, each having their own criteria for success. This leads to fragmented, non-repeatable processes and manual steps to deliver a pointed solution.
Key Takeaways:
- AI Engineering is a team sport
- Connect, orchestrate, learn and engage
- Build a scalable and repeatable process for the various personas involved in the AI lifecycle