Posts

The AI field is advancing in an incredible pace and 2022 has been shaken up by a few major announcements of algorithms able to generate fantastic images just by providing a text prompt.

Background Master data maintenance is a time-consuming activity for many businesses. Companies like retailers selling large amounts of different articles or manufacturing companies processing raw materials into finished goods can easily collect databases containing hundreds of thousands of master data items, which in turn may possess many hundreds of attributes.

In my previous blog post on reinforcement learning I demonstrated a way to get a gentle introduction into this field by using Keras-RL2. While writing that I found it quite difficult to get an overview of the many reinforcement learning frameworks available today which all have different levels of maturity.

The last few weeks I have been experimenting with deep neural networks for Reinforcement Learning. Reinforcement Learning is a way to learn to apply a sequence of events and has its root in control problems.

I recently had written a Python program to perform some performance calculations over the results from a regression model. The program was not too complex but the sheer number of calculations to be performed made it quite slow and resulted in a runtime of several hours.

Recently I dusted off some prior work on analyzing NYC yellow cab rides which I described in the following blog post: Timelapse data exploration of NYC Taxi rides A large chunk of time in setting up the solution from that post sits in loading the data into HANA using the Eclipse “Data from local file” import screen.

Back in 2014 the city of New York put online a dataset with yellow cab rides comprising a full year of data. Back then I remember struggling quite a bit with managing the sheer volume of the dataset involved, trying out various alternatives for reading in the full dataset.

This is part 2 in a two-part series of blogs on large-scale and explainable forecasting using APL. In part 1 I have outlined a way to utilize the APL library for in-database training of a regression model in HANA in order to be used together with an external Node.

Background Last year I became involved in a project for a retailer based in The Netherlands who had finished construction of a new distribution center. This new innovative DC is fully mechanized and is operated with a minimal amount of personnel, which is different from conventional distribution centers where sudden large order spikes are fulfilled by having more staff pick these orders in parallel.