Niels Bantilan

“Teams building a sovereign AI factory ask two things of the runtime underneath it. A run that dies on infrastructure at hour eleven has to pick up where it stopped instead of starting over, and the whole system has to run inside their own cloud account and pass a security review that assumes the vendor gets breached.

As AI applications and agents move from prototypes to production, Python developers are increasingly tasked with orchestrating large numbers of models, tools and external services.

Techniques like GRPO, combined with reinforcement learning with verifiable rewards (RLVR), have shown success in fine-tuning LLMs for reasoning tasks with a strong inherent reward, such as math and coding. However, can these techniques be more generally applied to other real-world tasks without a...

As the dust settles from the initial boom of applications using hosted large language model (LLM) APIs, engineering teams are discovering that while LLMs get you to a working demo quickly, they often struggle in production with latency spikes, context limitations, and explosive compute costs. This session provides a practical roadmap for navigating not only the experiment-to-production gap using small language models (SLMs), but also the AI-native orchestration strategies that will get you the most bang for your buck.

In the era of Foundation LLMs, a lot of energy has moved from the model training stage to the inference stage of the ML lifecycle, as we can see in the explosion of different RAG architectures. But has a lot changed in terms of the techniques to systematically improve performance of models at inference time?

Original Full Title: Validating and Testing R Dataframes with Pandera via reticulate: A Case Study in R-Python Interoperability Data science and machine learning practitioners work with data every day to analyze and model them for insights and predictions.

One of the biggest challenges for data scientists and machine learning engineers alike is the friction caused by the iteration cycle between prototyping and production. It’s not enough to deploy a working model to a serving app.

Data quality remains a core concern for practitioners of machine learning, data science, and data engineering, and in recent years specialized packages have emerged to validate and monitor data and models. However, as the open source community iterates on data frameworks – notably, highly performant entrants such as Polars – data quality libraries need to catch up to support them.

Data science and machine learning rely on high quality datasets for visualization, statistical inference, and modeling. However, the barriers to testing data processing, analysis, or model-training code are high, even with the extensive tooling that the python ecosystem offers, such as pandas , pytest , and hypothesis .