archive

Artificial intelligence (AI) and machine learning (ML) are key drivers of digital transformations in today’s organizations. Many companies want to benefit from AI and ML in hopes of turning data into value. As Venturebeat points out however, «Artificial intelligence (AI) in the lab is one thing; in the real world, it’s another. Many AI models […]

As a great number of today’s organizations increasingly design and implement software using data and AI solutions, a growing community has emerged with heightened awareness of the challenges in transitioning a product from development to production. This shift emphasizes the need for models to not only function effectively, but to meet the specific needs of

Before leveraging data and turning it into value, are you concerned with accessing the right data, transforming it, and setting up (ideally reliable, replicable, and secure) pipelines? At the same time, are you aiming to ensure data quality and smooth collaboration with other teams and stakeholders? Some stakeholders want to leverage data for business or

Building an effective machine learning pipeline is an essential part of the end-to-end MLOps lifecycle, which follows up the concept of building reliable data pipelines by applying DataOps, as explained in our previous blog post. A pipeline for machine learning is a set of instructions that manages the data flow into and out of ML

With more than 20 years of field experience and working with the leading IT partners, Digital Architects Zurich developed a blueprint to support digital transformation. In this blog post, we will present this blueprint for the Digital Highway for reliable and continuous software delivery and operations. The challenge of successful Digital Transformation In order to

According to Reuters, ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after launch, making it the fastest-growing consumer application in history. While this is phenomenal news, ChatGPT also crashed repeatedly, struggling with the usage load. OpenAI then announced a $20 monthly subscription.

If you let a software engineer come up with an ideal set-up for application operations, you will most likely hear about a concept that is very similar to that of Site Reliability Engineering (SRE) as Ben Treynor, VP of engineering at Google and founder of Google’s SRE team, describes.  The motivation to introduce and define such a role is based on conflicting goals in modern operating models. Two examples of such conflicts are: 

We have already touched on multiple points of the Machine Learning lifecycles in previous blog posts and our Next Generation Data & AI Journey powered by MLOps. In the last post, we talked about effective Data Science & Machine Learning pipelines. In this blog post, we will dive a little deeper into the quality aspects

You certainly have heard of a company named Google. The IT giant was among the first pioneers of Site Reliability Engineering (SRE) . After making its proofs within Google’s production systems, the concept gained a lot of attraction around the Software Engineering community. At Digital Architects Zurich, we understand Site Reliability Engineering as a fundamental concept, as we explained in a previous blogpost.

In the ever-evolving field of life sciences, the emergence of Large Language Models (LLMs) has brought about a revolutionary shift in how data can be processed, analyzed, and harnessed. LLMs, powered by state-of-the-art natural language processing (NLP) techniques, are able to generate and manipulate vast amounts of scientific text and data. By leveraging their immense