Google Cloud's tools and solutions such as BigQuery, AI and ML, and IoT, can facilitate the creation of a transformative data platform for ThyssenKrupp. These are the slides that Stiv presented in the workshop. They cover Google's data platform solution, a brief security introduction, and specific use cases.

Looker gives you all the tools you need to work with data in a wide variety of scenarios - from modern business intelligence and embedded analytics to workflow integrations and custom data applications. These are slides presented by Markus on the second day of our workshop.

Learn how to construct your systems to standardize and manage the life cycle of machine learning in production with MLOps on Google Cloud.

AI-driven organizations are using data and machine learning to solve their hardest problems and are reaping the rewards. But creating an ML model is the easy part—operationalizing and managing the lifecycle of ML models, data and experiments is where it gets complicated.

We often trust our high-accuracy ML models to make decisions for our users, but it’s hard to know exactly why or how these models came to specific conclusions.

We believe that for machine learning to achieve its positive potential, it needs to be participatory, involving the communities it affects and guided by a diverse set of citizens, policy-makers, activists, artists and more. Check out the tools here.

We consistently hear from our customers that they need new ways to apply the latest technologies, such as AI, to improve efficiency. One area that AI has proven to be particularly beneficial is in helping to automate the visual quality control process for manufacturing customers.