Probabilistic Forecasting with LightGBM and Dask

Use LightGBM to make probabilistic predictions of a continuous variable. We cover aspects about the model architecture, training and evaluation that are specific for probabilistic...

Building a Conversational AI with RAG

Learn how to build an intelligent conversational AI system that combines RAG with your own documents, deployed on Kubernetes. This guide demonstrates how to create...

Optimal asset reallocation strategies

Investigate strategies for reallocating investments from a risk-free asset class to a risky class. This article uses stochastic modeling, including Geometric Brownian motion, to analyze...

Efficient Likelihood Function Reparametrization for Regression against Categorical Variables

We simplify the likelihood function obtained when regressing on categorical variables. This speeds up the variational inference implementation in tensorflow.

Monitor deployed tensorflow models with Prometheus and Grafana

We provide a minimal example how to serve tensorflow models on a Kubernetes cluster and monitor them with Prometheus and Grafana. To expose the models...

MLflow on Kubernetes

We will containerize and deploy a MLflow server on a Kubernetes cluster on Google cloud. We will also create the MLflow backend DB, the artifact...

Bayesian inference for stochastic processes: an analytically solvable problem

We explain the application of the Bayesian inference approach to the case of having multiple trajectories of a stochastic process. We will consider an analytically...

Variational inference in probabilistic models: an analytically solvable example

The Bayesian inference approach gives us the opportunity to systematically combine and update our prior beliefs about the model parameters with new evidence. In the...

Geospatial data visualization

Visualize random locations, vehicle trajectories and vehicle telematics data.

Ad auction bidding strategy

Real-Time Bidding (RTB) has become a relevant paradigm in display advertising. It mimics stock exchanges and utilizes computer algorithms to buy and sell ads in...

Track the model performance metrics in Federated training

Federated learning is a machine learning technique that trains a model across multiple decentralized devices, each of them holding a local data sample, without exchanging...

Navigation through a dynamic map using the Bellman equation

We will look at the problem of navigating through a dynamically changing map. It can be represented as a sequence of optimization problems for every...