Transformers are everywhere: NLP, Computer Vision, sound generation and even protein-folding. Why not in forecasting? After all, what ChatGPT does is predicting the next word. Why this architecture isn't state-of-the-art in the time series domain?

In this talk, you will understand how Amazon Chronos and Salesforece's Moirai transformer-based forecasting models work, the datasets used to train them and how to evaluate them to see if they are a good fit for your use-case.

For some natural language processing (NLP) tasks, based on your production constraints, a simpler custom model can be a good contender to off-the-shelf large language models (LLMs), as long as you have enough qualitative data to build it. The stumbling block being how to obtain such data? Going over some practical cases, we will see how we can leverage the help of LLMs during this phase of an NLP project. How can it help us select the data to work on, or (pre)annotate it? Which model is suitable for which task? What are common pitfalls and where should you put your efforts and focus?

In the last year there hasn’t been a day that passed without us hearing about a new generative AI innovation that will enhance some aspect of our lives. On a number of tasks large probabilistic systems are now outperforming humans, or at least they do so “on average”. “On average” means most of the time, but in many real life scenarios “average” performance is not enough: we need correctness ALL of the time, for example when you ask the system to dial 911.

In this talk we will explore the synergy between deterministic and probabilistic models to enhance the robustness and controllability of machine learning systems. Tailored for ML engineers, data scientists, and researchers, the presentation delves into the necessity of using both deterministic algorithms and probabilistic model types across various ML systems, from straightforward classification to advanced Generative AI models.

You will learn about the unique advantages each paradigm offers and gain insights into how to most effectively combine them for optimal performance in real-world applications. I will walk you through my past and current experiences in working with simple and complex NLP models, and show you what kind of pitfalls, shortcuts, and tricks are possible to deliver models that are both competent and reliable.

The session will be structured into a brief introduction to both model types, followed by case studies in classification and generative AI, concluding with a Q&A segment.