A Study on using a Rust-based dynamic Module system in WebAssembly for processing Data

I have also published the source code of this on Codeberg (EU Git hosting) and Github (US Git hosting). Nowadays we have a plethora of programming languages and platforms at our fingertips. They have different advantages and disadvantages depending on the use case and preferences. Often many different combinations of components are used for data… A Study on using a Rust-based dynamic Module system in WebAssembly for processing Data weiterlesen

Modern Cloud Application Delivery: WASM and WASI

I described in a previous blog post that modularity will play a key role in future enterprise applications. This is demonstrated in the current trends of serverless functions or containerized architectures. However, those solutions are not perfect: Given the trend of many different computing architectures, such as ARM on servers, Internet of Things (IoT) Edge… Modern Cloud Application Delivery: WASM and WASI weiterlesen

Semantic Versioning for Artificial Intelligence (AI) 1.0.0

Artificial Intelligence (AI) becomes more and more part of some applications catering for the needs of many people. While AI is part of software products it has a very different velocity and less predictable needs for change. Especially if it addresses open-ended domains, such as natural language processing (NLP), where the content can change or… Semantic Versioning for Artificial Intelligence (AI) 1.0.0 weiterlesen

The Question of Maintenance of pre-trained Machine Learning Embeddings

I will address in this post the issue of maintenance of large pretrained embeddings within Artificial Intelligence (AI) services. While this issue has some links to ethical aspects (see for example the European Commission’s guidelines on trustworthy AI or here), the focus here is on maintainability of those embeddings as part of MLOps. Software Maintenance… The Question of Maintenance of pre-trained Machine Learning Embeddings weiterlesen

Secure Blockchain Analytics

Blockchain analytics has become a trending topic in recent years. This topic is of interest not only for public blockchains, such as Bitcoin or Ethereum and their Altcoins, but also for private/permissive blockchains based on various technologies. Nevertheless, there are many challenges involved, such as the large data volumes, the inefficient format for analytics, state… Secure Blockchain Analytics weiterlesen

AI Applications and Systems for Deep Logic and Probabilistic Networks

This blog post describes the integration of deep learning, logic and probabilistic reasoning to enable advanced artificial intelligence tasks. The combination of completely different set of AI approaches will be one of the key advances to support AI driven business processes in the coming years. Furthermore, I describe challenges for operating such complex AI systems… AI Applications and Systems for Deep Logic and Probabilistic Networks weiterlesen

Unikernels, Software Containers and Serverless Architecture: Road to Modularity

This blog post is discussing the implications of Unikernels, Software Containers and Serverless Architecture on Modularity of complex software systems in a service mesh as illustrated below. Modular software systems claim to be more maintainable, secure and future proven compared to software monoliths. Software containers or the alternative MicroVMs have been proven as very successful… Unikernels, Software Containers and Serverless Architecture: Road to Modularity weiterlesen

GPUs, FPGAs, TPUs for Accelerating Intelligent Applications

Intelligent Applications are part of our every day life. One observes constant flow of new algorithms, models and machine learning applications. Some require ingesting a lot of data, some require applying a lot of compute resources and some address real time learning. Dedicated hardware capabilities can thus support some of those, but not all. Many… GPUs, FPGAs, TPUs for Accelerating Intelligent Applications weiterlesen

Collaborative Data Science: About Storing, Reusing, Composing and Deploying Machine Learning Models

Why is this important? Machine Learning has re-emerged in recent years as new Big Data platforms provide means to use them with more data, make them more complex as well as allowing combining several models to make an even more intelligent predictive/prescriptive analysis. This requires storing as well as exchaning machine learning models to enable… Collaborative Data Science: About Storing, Reusing, Composing and Deploying Machine Learning Models weiterlesen

Automated Machine Learning (AutoML) and Big Data Platforms

Although machine learning exists already since decades, the typical data scientist – as you would call it today – would still have to go through a manual labor-intensive process of extracting the data, cleaning, feature extraction, regularization, training, finding the right model, testing, selecting and deploying it. Furthermore, for most machine learning scenarios you do… Automated Machine Learning (AutoML) and Big Data Platforms weiterlesen