I'm also considering to include Conversation Analytics from GCP
However, collection and storage of these game events bring a variety of challenges due to its sheer volume and high speed.
To address the challenges, we have developed an in-house product called Kingestor, which allows us to ingest our game events in an effective manner. It processes game events in near real-time with just a ten-minute latency, loading approximately five million events per second. Kingestor ensures data integrity through event reconciliation and deduplication, providing accurate, real-time insights for both business and technical applications. It is a scalable and adaptable product, which is designed for use across the gaming industry, making it easy to implement for businesses handling large-scale data.
On the other hand, fine-tuning offers a path to tailor models for domain-specific tasks or nuanced interactions, delivering consistent performance even with smaller models. While it demands more resources upfront, fine-tuned solutions can lead to significant long-term savings by reducing reliance on oversized models:
Exploration how our system uses a graph-based approach to store transactions and enhance fraud controls with advanced features, boosting the effectiveness of both ML models and static rules. Presentation of key components of the system, including a real-time feature computation service optimized for low latency, a visualization tool for network analysis, and a mechanism for historical feature reconstruction.
In 2023, the Data Engineering team @ SumUp adopted a Platform mindset. The plan was great but the journey wasn't as smooth sailing as it should be.
Data users lost trust to our team and started to do workarounds.
GenAI has been with us for a while - enough for a number of actual systems to be deployed into production. Moving beyond the initial hype about it, this roundtable will tackle the real challenges and best practices of running GenAI systems in real-world, related to:
We invite both engineering and business leaders, architects, and AI practitioners that already deployed GenAI systems to production, as well the ones that have to yet turn their proofs-of concepts into serious deployments and would like to get to know how to do so - but the knowledge of the basics of such systems and aspects around them are required.
Essentially - we would like to gather a group of really component people for discussion, exchange of knowledge, and establishing best practices for GenAI systems.
This session is designed for technical experts and decision-makers to exchange experiences, insights, and predictions about the evolving landscape of LLMs. Whether you’re actively using one of these approaches or still considering your options, this presentation/discussion will offer valuable perspectives to help you make informed decisions.
In many cases companies tend to hire admins who manage these ad-hoc, with best backup being notepad audit trace of what they run. Configuration in such case differs per user and inconsistencies are piling up. Some are smarter and implement some Git-based solutions, like Terraform. Tools like Terraform typically have SnowFlake plugin to manage these all, but they lack templating, are always behind the latest SnowFlake SQL extensions and do not really address self-service (ad-hoc or UI-based) management needs.
In IQVIA DTE we came to the fairly good compromise between various security / auditing needs, leaving space to both automation, enforcement & self-service where appropriate, yet coming up with a very neat & simple solution which I would like to present.
In modern data-sharing paradigms like data mesh, it is highly desirable to automate the verification of data access requests. This is typically achieved by formalizing constraints into data contracts using domain-specific languages. However, these constraints originate from legal documents or internal policies. Translating these documents into formal data contracts is a tedious and error-prone process that must be repeated whenever the source documents are updated.
To address this challenge, we have implemented a request checker leveraging large language models as part of an open-source data governance platform (https://github.com/datacontract-manager). Our system evaluates requests based on the relevant policies, the type of data requested, and the context of the request. This way, our platform is able to correctly detect potential data protection violations and to provide correct explanations for rejections.
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