Blog Archives

12 Mar 2026

Building Production-Ready AI Agent Systems with AgentOps

Most AI agent systems fail not because the models are weak, but because they lack an operational foundation. This session explains AgentOps around two core pillars: MLflow as the system of record for agent lifecycle management, and an AI Gateway as the control plane for routing, governance, and cost management. We will explore how to […]

23 Feb 2026

Harnessing the Power of Generative AI for Intelligent Applications

Generative AI is transforming industries by enabling businesses to create content, automate decision-making, and enhance user experiences. But how can organizations effectively build and deploy AI-powered solutions while maintaining security, efficiency, and scalability? In this session, we will explore the fundamentals of Generative AI models, their real-world applications, and how they can be leveraged to […]

19 Feb 2026

Signals, Systems, and Scams: Deconstructing the Future of Financial Intelligence

As global financial platforms evolve, the “Trust Gap” becomes an engineering challenge. Scaling a product to millions of users requires more than just better models—it requires a fundamental shift from manual heuristics to automated, high-fidelity intelligence. We’re moving beyond the basics of model training to explore the “Last Mile” of production ML: the infrastructure of […]

11 Feb 2026

Self-Hosted LLMs: Running Your Own Inference Infrastructure

When does it make sense to run your own LLM inference infrastructure instead of paying per-token to third-party APIs like OpenAI or Anthropic? This session gives you a practical framework for that decision and the technical foundations to execute it. We’ll ground everything in three concrete use cases – interactive chat, batch document processing, and […]

11 Feb 2026

Human, Machine, Data, AI Symbiosis

For well over 50 years, our machines, our data, and our (private/working) lives have coexisted in well-insulated boxes. However, the emergent behaviours of AI and robotics, along with our needs as individuals and a society, demand a full symbiosis if we are to succeed in a future where technology-driven change maintains its exponential growth. So […]

11 Feb 2026

How to Make Google Trends Data Actually Usable for Machine Learning

Google Trends data is messy. It’s normalised, it’s abstracted and it’s aggregated to a very coarse layer. In this talk we’ll discuss how to turn Google Trends snapshot into large datasets you can actually use to compare terms across countries in a meaningful way by using a chaining methodology and borrowing tech from the world […]

10 Feb 2026

Beyond Explainability: How Causal Thinking Builds Trustworthy Data Science

Data scientists are deeply invested in producing trustworthy insights but as AI becomes more powerful and autonomous we’re being confronted with the question: How do we know our insights are truly reliable? While explainable AI techniques have helped make black-box models more interpretable, interpretability alone doesn’t guarantee trustworthiness. In this talk, we’ll explore why trustworthy […]

29 Jan 2026

What Recommender Systems Really Optimise For: Metrics, Feedback Loops and Echo Chambers

Recommender systems sit behind almost every feed and “For You” page, yet most teams still optimise them for a narrow set of metrics like CTR or watch time. The result is a gap between what dashboards say is “good” and what users, regulators and society actually care about: satisfaction, trust, and not being trapped in […]

29 Jan 2026

Beyond Benchmarks 2.0: A Practical Framework for Measuring Multimodal and Agentic AI Success

While most enterprise AI projects start with excitement, only 20% survive the move from demo to production. This updated session evolves the “Beyond Benchmarks” framework for 2026, moving beyond text-only RAG to address the complexities of multimodal and agentic systems. We will explore how to measure success when AI interacts with images and complex documents, […]