With the heightened attention on LLMs and what they can do, and the widening impact of AI on day-to-day life, the push by regulators across the globe to regulate AI is intensifying. As with GDPR in the privacy realm, the EU is leading the way with the EU Artificial Intelligence Act (AIA). Regulators everywhere will be looking to the AIA as precedent, and understanding the requirements imposed by the AIA is important for all players in the AI channel. Although not finalized, the basic framework regarding how the AIA will work is becoming clearer. The impact on developers and deployers of AI (‘providers’ and ‘users’ under the AIA) will be substantial. Although the AIA will probably not go into effect until early 2025, AI applications developed today will likely be affected, and design and development decisions made now should take the future regulations into account. In this session, we Matteo Quattrocchi, Brussels-based Director, Policy – EMEA, for BSA (the Software Alliance – the leading advocacy organization representing the enterprise software sector), will present an overview of the current proposed requirements under the AIA and give an update on the ongoing deliberations and likely timing for enactment. We will also highlight some of the ways the Lakehouse platform, including Managed MLflow, can help providers and users of ML-based applications meet the requirements of the AIA and other upcoming AI regulations.

Talk by: Matteo Quattrocchi and Scott Starbird

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Databricks has been an essential part of the Conde Nast architecture for the last few years. Prior to building our centralized data platform, “evergreen,” we had similar challenges as many other organizations; siloed data, duplicated efforts for engineers, and a lack of collaboration between data teams. These problems led to mistrust in data sets and made it difficult to scale to meet the strategic globalization plan we had for Conde Nast.

Over the last few years we have been extremely successful in building a centralized data platform on Databricks in AWS, fully embracing the lakehouse vision from end-to-end. Now, our analysts and marketers can derive the same insights from one dataset and data scientists can use the same datasets for use cases such as personalization, subscriber propensity models, churn models and on-site recommendations for our iconic brands.

In this session, we’ll discuss how we plan to incorporate Unity Catalog and Delta Sharing as the next phase of our globalization mission. The evergreen platform has become the global standard for data processing and analytics at Conde. In order to manage the worldwide data and comply with GDPR requirements, we need to make sure data is processed in the appropriate region and PII data is handled appropriately. At the same time, we need to have a global view of the data to allow us to make business decisions at the global level. We’ll talk about how delta sharing allows us a simple, secure way to share de-identified datasets across regions in order to make these strategic business decisions, while complying with security requirements. Additionally, we’ll discuss how Unity Catalog allows us to secure, govern and audit these datasets in an easy and scalable manner.

Talk by: Zachary Bannor

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Within Wehkamp we required a uniform way to provide reliable and on time data to the business, while making this access compliant with GDPR. Unlocking all the data sources that we have scattered across the company and democratize the data access was of the utmost importance, allowing us to empower the business with more, better and faster data.

Focusing on open source technologies, we've built a data platform almost from the ground up that focuses on 3 levels of data curation - bronze, silver and gold - which follows the LakeHouse Architecture. The ingestion into bronze is where the PII fields are pseudonymized, making the use of the data within the delta lake compliant and, since there is no visible user data, it means everyone can use the entire delta lake for exploration and new use cases. Naturally, specific teams are allowed to see some user data that is necessary for their use cases. Besides the standard architecture, we've developed a library that allows us to ingest new data sources by adding a JSON config file with the characteristics. This combined with the ACID transactions that delta provides and the efficient Structured Stream provided through Auto Loader has allowed a small team to maintain 100+ streams with insignificant downtime.

Some other components of this platform are the following: - Alerting to Slack - Data quality checks - CI/CD - Stream processing with the delta engine

The feedback so far has been encouraging, as more and more teams across the company are starting to use the new platform and taking advantage of all its perks. It is still a long time until we get to turn off some of the components of the old data platform, but it has come a long way.

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