Digital Transformation (13. - 19.3.2023)

Two webinars:

• Architecture, products, and total cost of ownership of the leading machine learning stacks
• Data stewards  – defining and assigning

Data observability vs. data monitoring vs. data testing

Hybrid cloud wins - and the difference between "or" and "and" mindset

Structuring machine learning project with MLOps in mind

MLOps tips and tricks - 75 code snippets

Using MLflow with ATOM to track machine learning experiments

The difficulties of monitoring machine learning models in production

Are expert systems dead? - a review of recent trends, use cases and technologies

Machine customers represent one of the biggest new growth opportunities of the decade

ChatGPT - understanding what it can do and what it cannot do

Data as Achilles heel of AI - hindering AI's effectiveness with data silos and dark data