AWS-Based IoT Kick-Starter Platform
A validated IoT architecture pattern and the AWS Cloud Development Kit expedite IoT use case tests and enable reusability
Automated image processing: A standard architecture
The model has been trained – so what's next? We'll show you which cloud architecture allows scalable analysis and processing of images.
Brief Guide to Using Generative AI and LLMs
This blog post shows you the requirements and steps necessary to effectively use generative AI in such contexts.
Large language models – an overview
The landscape of large language models is changing rapidly. Not every model is suitable for companies. Here you get an overview.
Google IoT core's end of life - Part 3
This blog post shows you how Google's IoT Core can be replaced with the help of Stackable's open-source data platform!
Google IoT core's end of life – AWS or Azure? - Part 2
On the search for alternatives to Google's IoT core - this blog shows how you can make use of the services of AWS or Azure!
End of Google IoT core's life – looking for alternatives?
Many Google cloud users are now asking themselves: What are my alternatives and how do I integrate them into my existent architecture?
Computer vision 101: How machines learn to see
What really happens in the black box of computer vision? We'll show you how machines can reliably recognize and analyze images.
Deliver projects faster with Python Ibis Analytics
The road from successful PoC for a data-analysis pipeline to production is often long. We'll show you how to shorten it with Python Ibis.
LightGBM on Vertex AI
In the Google cloud, Vertex AI is the MLOps framework. It is very flexible, and you can basically use any modelling framework you like.
Use of private Python packages in Vertex AI - 3
Structure your model training with Python packages in Google's cloud platform.
Vertex AI pipelines and their benefits - 2
You already know how to set up a Vertex AI pipeline. Now you will discover the advantages of training your models in pipelines.
Vertex AI pipelines - getting started - 1
Do you want to set up a fully automated Vertex AI ML pipeline? We'll show you the first steps.
Ray in the Google cloud – part 2
Now it’s time to configure our cluster and take it for a ride, by computing one of the famous (and beautiful!) Mandelbrot sets.
Ray in the Google cloud – part 1
A Ray cluster in the Google cloud can greatly profit from some of Google’s proprietary tools to be more secure. We show how.
Machine learning in the cloud – data ingestion pipelines
Learn what to consider when using training data from the cloud, and how reading can be implemented efficiently.
Quantile Regression with Gradient Boosted Trees
Combining quantile regression with gradient boosted trees yields a versatile modelling
tool. Let's see how it was implemented in LightGBM!
IoT data processing – part 2
Data processing in the cloud – do you want to know how to implement serverless IoT data processing in Azure? Then check out our architecture!
IoT data processing - part 1: Azure Synapse Analytics
Data processing in the cloud – the first article contains our architecture recommendations with Azure Synapse Analytics.
How to install Ray under Windows
Ray enjoys a growing popularity in the ML community. Getting it up and running under Windows can be tricky however. This blog tells you how.
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