Seven Innovative Trading Apps and Seven Best Practices You Can Steal
Discover the secrets of the high-stakes world of capital markets, where trillions of events are processed daily, there’s no room for delay in our latest eBook.
Discover the future of data management in Wall Street with our new eBook and dive into the evolution of streaming analytics, time-series data management, and generative AI.
KX Launches KDB.AI Server Edition For Enterprise-Scale Generative AI
KX has announced the general availability of KDB.AI Server, a highly-performant, scalable, vector database for time-orientated generative AI and contextual search.
Accelerating Quant Business Growth and Capture New Revenue Stream with KX and AWS
As financial institutions aim to expand their quant business and adopt higher frequency strategies, they often encounter significant infrastructure challenges. Existing solutions can struggle with performance and latency issues and consolidating real-time and historical data management becomes crucial. These hurdles impede their ability to quickly capture market opportunities and scale their operations effectively.
During this event, as we indulge in food, drinks, and networking with peers and experts, we will delve into
discussing on addressing this challenge and how to achieve faster time to market, enhance data performance, and focus on high-value analytics
sharing how a customer alleviates these burdens and enabled aggressive growth plans that helps to capture market opportunities more efficiently
including a live demo on the scalability and pattern matching with the solution from KX and AWS
Register your interest today and we will be in touch with you to confirm your seat. We hope to see you there!
* By submitting your registration, you consent to sharing your contract information with Amazon Web Services (AWS) and ICE. This data will be used in line with our privacy policy and those of the mentioned entities.
KX’s Generative AI Meetups are for developers in all stages of their AI journey. We focus on aiding the use of AI in production applications through talks from various companies with specialized knowledge of AI development.
This meetup will focus on “Embeddings at Scale” which is a crucial topic for deploying production GenAI use cases, especially with vector databases. We have a great list of speakers and hope you can join us to further your knowledge and network with peers in the industry!
Scaling Temporal Similarity Search for Technical Analysis – Michael Ryaboy, Developer Advocate, KDB.AI
Michael will go over how Temporal Similarity Search can be used for Technical Analysis, anomaly detection, and scaled to hundreds of millions of vectors.
Effortlessly Infinite Embeddings with Modal – Charles Frye, AI Engineer, Modal Labs
In this talk, Modal AI Engineer Charles Frye will share projects built using embeddings, including a recommendation system for a simulated Twitter, vibes-based search of California, and more.
Designing a Scalable Distributed Cache for ML Training Datasets in the Cloud – Bin Fan, Founding Member, Alluxio
Bin Fan, Founding Engineer at Alluxio, will go over insights from building a distributed cache for ML training. In the rapidly evolving landscape of machine learning, efficiently managing and accessing large datasets is critical for training models at scale. In this session, Bin Fan, Founding Engineer at Alluxio, will share insights from the journey of creating a scalable and reliable distributed cache specifically designed for ML training datasets and checkpoints on popular frameworks like PyTorch, Ray, and TensorFlow. The talk will explore the distinct requirements of machine learning compared to traditional big data analytics—where Alluxio was initially developed. Bin will highlight the challenges posed by ML-specific data access patterns, diverse data formats, and the need for optimal resource management in these environments. He will discuss how these factors influenced the design and optimization of a distributed cache that meets the stringent demands of modern ML workloads. The session will conclude with an analysis of benchmark results, showcasing the performance gains and scalability improvements achieved through this distributed caching solution, and how it leads to enhanced GPU utilization and overall efficiency in ML training pipelines.
Scaling LLM and embedding generation workloads with Ray – Amog Kamsetty, ex Founding ML Engineer, Anyscale
7:30pm – 9:00pm Networking & Complimentary Food and Beverages
Venue
Address
Marigold Event Space
194 Church St. San Francisco, CA 94606
The Marigold entrance is just to the right of Churchill’s main door – please look for a black gate-door and a security guard.
