Data infrastructure is rapidly growing and evolving along with infrastructure for AI/ML, with the latter growing largely independent from the former. An emerging generation of AI/ML tooling emphasizes data-centric versus model-centric approaches to the ML development lifecycle. These tools recognize that data is the foundation for AI and seek to open opportunities for all data professionals to participate by eliminating the unnecessary complexity of traditional model-centric solutions.

TL;DR: To add events monitoring with Crashlytics to a Unity Android app, you’ll first need to prepare a Firebase project, then add the Firebase plugin to Unity and configure Unity to work with Firebase. Finally, you also need to update your CI/CD pipeline to support the changes. Let’s see how to integrate Firebase Crashlytics into a Unity Android app to monitor and troubleshoot errors easily!

For the newest instalment in our series of interviews asking leading technology specialists about their achievements in their field, we’ve welcomed Katie King, CEO of AI in Business, a firm that specializes in AI consultancy and training. With over 30 years of experience, Katie has advised many of the world's leading brands and business leaders, including Virgin, o2, Orange and Accenture.

This guide will show you how to easily add Continual as the AI layer to your modern data stack with Snowflake at the core. The intention is to provide an introduction to using Continual on Snowflake. After completing this tutorial, users are invited to try more advanced examples. We are going to demonstrate connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining the predictive model continuously over time.

Organizations today have access to a wide stream of data. Data is generated from recommendation engines, page clicks, internet searches, product orders, and more. It is necessary to have an infrastructure that would enable you to stream your data as it gets generated and carry out analytics on the go. To aid this objective, incorporating a data pipeline for moving data from Apache Kafka to BigQuery is a step in the right direction.

MODAS is the world’s first Mobile DevOps, Performance, Productivity, and Maturity Assessment. What does it mean for teams wanting to gain more insight into optimizing their end-to-end DevOps processes?