Ramesh Dontha, DX Pro | Kfir Yeshayahu, Veritone | Christopher Bergh, DataKitchen | Alok Aggarwal, Scry Analytics
AI and machine learning are closely linked to digital transformation, and is perhaps why 2021- a year of immense business transformation - has seen such a shift towards MLOps and ModelOps. In fact, according to a recent IBM survey, 21% of respondents said that AIOps had a “transformational” relationship between IT and other parts of the business.
Some key takeaways from organisations implementing MLOps include observability and end-to-end visibility, automation of complex processes, and support on the cloud. Evidently, MLOps and ModelOps can have a real impact on the relationship between moving parts in the organisation, and done right, can have a transformative impact on your business.
Join us in this session as we discuss:
- How MLOps and ModelOps have impacted transformation efforts in 2021
- Best practices for implementing MLOps in your organisation
- Overcoming challenges when it comes to implementation
- ModelOps challenges and how to overcome them
- What’s next for MLOps and ModelOps as we move into 2022