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Unlock the secrets to scaling production RAG! This case study dives into our successful deployment of a low-latency RAG chatbot built on Milvus, handling over 10 million vector records. Discover how we managed a 12-18 node Kubernetes cluster powered by cost-effective AWS Graviton CPUs and aggressively used Spot Instances to stay within budget while achieving high accuracy.
Have more than a decade of experience in leading cloud infrastructure of various SaaS applications. Currently serving as an Engineering Manager in software consultancy firm.
Trallie is an open-source framework backed by the NGI Search Consortium that leverages the power of large language models (LLMs) to reimagine information extraction (IE) with or without user guidelines. Instead of relying on labelled examples or copious amounts of training on your data collection, Trallie requires very few or no representative examples of the data to convert into a structured format. It has three core objectives:
Cristiano is a Theoretical Physicist with a PhD in Quantum Information Theory from SISSA, Italy. With 10 years of experience in Deep Learning and AI, he is currently the Lead AI Scientist at Pi School and Tech Lead of two ESA grants. He is a lecturer in Deep Learning at the MHPC (ICTP/SISSA... Read More →
Vijayasri Iyer is a Machine Learning Scientist at Pi School, where she has led multiple international teams in developing Generative AI solutions. She holds a Bachelor’s degree in IT, Master’s in AI and certifications in Technology Policy and AI Safety.
Generative AI workloads have introduced a new class of infrastructure challenges—massive model sizes, unpredictable bursty inference, tight latency budgets, and soaring GPU costs. While teams invest heavily in model compression and distillation, they often overlook the other half of the equation: the ML pipeline.
Shashidhar Shenoy is a software engineer and technical leader specializing in distributed systems, AI/ML infrastructure, and scalable authentication platforms. With over a decade of experience, he has led high-impact projects, including optimizing cloud infrastructure for AI/ML workloads... Read More →
As a Sr. Machine Learning Engineer at Torc Robotics, I am building critical ML infrastructure for the L4 self-driving class-8 trucks, paving the way for safer transportation of freight.
In our talk, we present a practical use-case within the 8RA (https://www.8ra.com/) program, the IPCEI covering Cloud and Infrastructure Services (CIS), built on top open-source software. Our use-case exploits both ML and GenAI to support scenarios within Multi-Provider Cloud Edge Continuum, cloud infrastructure federation, automated service request and allocation of applications to edge sites properly selected. We design an architecture whose core is a set of AI agents, which collaborate to accomplish different tasks, ranging from application deployment file generation up to site prediction for application deployment, according to service requests taken as input by users in natural language. Specifically, our architecture currently includes several agents: LANE (Language-to-Action Neural Engine), responsible for processing user requests expressed in natural Language; Placement, providing the optimal edge site recommendation for each application, ensuring maximum energy efficiency once deployed. It leverages an AI-driven approach to predict energy consumption and optimize resource allocation, improving the sustainability of cloud services.
