DeveloperWeek 2025

Moshe Twitto, Pliops, Founder & CTO

Organizations are increasingly concerned about the lack of power budgets in data centers, particularly as AI infrastructure and emerging AI applications lead to higher energy footprints and strain cooling systems. As they scale their AI operations and add GPU compute tiers, the escalating power and cooling demands, coupled with significant capital investments in GPUs, are eroding margins. A monumental challenge looms as data centers struggle to secure essential power, creating significant pressure for companies striving to expand their AI capabilities.

In today's LLM inferencing computing, GPU prefill operations are heavily compute-bound and critically determine the batch size. While prefill can fully utilize GPU resources, increasing the batch size beyond a certain point only increases the Time to First Token (TTFT) without improving prefill rate. On the other hand, GPU decode operations are HBM bandwidth-bound and mainly influenced by model and KV cache sizes, benefiting significantly from larger batch sizes through higher HBM bandwidth efficiency. Pliops' solution improves prefill time, allowing for larger batch sizes without violating user SLA for prefill operations. This enhancement directly affects decode performance as well, as it gains greatly from the increased batch size. As a result, by improving prefill time, the system achieves nearly proportional improvements in end-to-end throughput.

Parth Shukla, Cequence Security, Security Analyst
Khyati Ganatra, Cequence Security,  Manager, Applied ML

In the presentation titled "AI Frontiers: Shielding Digital Gateways from Bot Invasions," we delve into the forefront of cyber defense against bot-driven threats that exploit API vulnerabilities. This comprehensive study explores how advanced AI and ML models are being harnessed to fortify digital defenses, offering a detailed analysis of API communication patterns and the evolving landscape of bot attacks. Through a series of real-world case studies, we illuminate the mechanisms of sophisticated bot strategies—ranging from data breaches and account takeovers to shopping bots that deplete inventories. The narrative progresses to unveil how AI/ML technologies serve as the cornerstone of innovative defense mechanisms. We dissect the architecture of AI-driven systems tailored to detect and counteract anomalous behaviors indicative of bot activities, leveraging vast datasets to train ML models that adeptly differentiate between legitimate user interactions and malicious bot intrusions. The discussion further navigates through the technical and operational nuances of implementing AI/ML defenses, emphasizing predictive analytics for preemptive action, machine learning for dynamic threat adaptation, and the overarching impact of such technologies in securing digital ecosystems against the insidious threats posed by automated attacks. This presentation not only highlights the challenges but also showcases the resilience and adaptability of AI/ML solutions in the ever-evolving battle against digital villains.

Oz Katz, Treeverse, CTO & Co-Founder

Langchain has become one of the most popular frameworks for anyone building custom, generative AI-driven apps powered by LLMs, that leverage RAG for the most enhanced results. But like all data products, these applications are really only as good as the org data fed into them.

In this live session you’ll learn how to build a reproducible AI app pipeline with Langchain & lakeFS, including how to build a RAG chatbot, while iteratively tuning it for best results leveraging Delta Lake’s temporal versions, you’ll come away with improved methods for data reproducibility for custom AI apps.