Machine Learning

Machine Learning

The Machine Learning stage delved into the techniques, algorithms, and practical applications that are revolutionizing the world of data and artificial intelligence. Industry experts explored the latest innovations—from predictive models to generative AI—along with algorithm optimization and data interpretability. It was an opportunity to discover how machine learning is transforming industry, research, and business.

Room Hosting

Roberto Digennaro
Roberto Digennaro
Senior Data Analyst
Emanuele Arosio
Emanuele Arosio
CEO & Founder
DataRank
4 JUNE
5 JUNE
6 JUNE
04 june 08:30
05 june 08:30
06 june 08:30
04 june 11:50 - 12:30
40 min
The evolution of AI has largely been shaped by advancements in compute power. However, an equally critical factor—memory—has emerged as a defining bottleneck for the next generation of AI infrastructure. While GPUs and TPUs have seen exponential improvements in FLOPS, memory bandwidth and capacity have struggled to keep pace. Today, training and inference at scale are constrained as much by memory limitations as by compute. The financial implications are staggering: High-Bandwidth Memory (HBM) now costs nearly as much as compute, and memory bandwidth is one of the leading constraints in large-scale AI deployments. The infrastructure of tomorrow must be designed with memory as a first-class consideration. This keynote explores the increasing role of memory in AI workloads, real-world examples of memory bottlenecks, and strategies for designing AI infrastructure that balances compute and memory effectively.
04 june 12:40 - 13:20
40 min
A custom AI chatbot that knows exactly what you need because it’s trained on your own data. We implemented MiniPilot, and it’s open-source. MiniPilot is not just an application — it’s a powerful architecture for building personalized AI chatbots. It combines the speed of Redis for vector retrieval with the power of models like ChatGPT, creating a scalable, fast, and user-friendly system. Generative AI and Redis are revolutionizing knowledge management, and MiniPilot is at the heart of this evolution.
04 june 14:00 - 14:40
40 min
Today, talking about artificial intelligence often automatically means discussing multimodal LLMs. Fueled by hype and widespread inexperience, these ultra-powerful models are often used even for simple tasks where much lighter (and more efficient) solutions would be more than enough. In this talk, we will pragmatically explore how to integrate AI at the product level, evaluating when it makes sense to leverage an LLM and when smaller models actually bring greater benefits in terms of cost, performance, and technical sustainability.
04 june 14:50 - 15:30
40 min
Internet is the single biggest barrier to digital education access and AI penetration. I will talk about how we are providing AI teachers running on complete LLMs that work without the Internet. Our AI teachers have upskilled students of 13 universities, are empowering learners in 4 continents, and in 2025 are working with 2 national governments to upskill their nation. The talk will discuss the path of this innovation and education design breakthroughs. It will also present a technical explanation of how to run AI without active Internet connections to access over 3 billion people who still do not use mobile Internet (Telemedia, 2023).
04 june 15:40 - 16:20
40 min
In the realm of edge AI, the conventional wisdom suggests that smaller models naturally translate to faster, more efficient performance on resource-constrained devices. However, my talk will challenge this assumption by unveiling the "size-performance paradox": the reality that choosing a smaller model doesn’t always yield the expected improvements in speed, energy consumption, or real-time reliability. I’ll demonstrate how hardware-aware deep learning compression and careful optimization balance model size with real-world performance, sharing practical insights and case studies for deploying AI on resource-constrained devices. Participants will gain a comprehensive understanding of how to effectively tailor AI models for edge deployment.

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