Blog & News

In this blog post, we'll introduce how to deploy LLMs into production system using MLOps best practices.

In this blog post, we'll introduce the concept of a data mesh in a high level and one of its main building block, data as a product.

Start your Data Mesh implementation with data as a product notion using data contracts and tests make use of DataOps best practices.

This article explores the implementation of Large Language Models (LLMs) in business, emphasizing LLMOps, MLOps, and Site Reliability Engineering. It discusses options like using existing LLM services versus self-hosting and methods including Out-of-the-Box, Fine-Tuning, and Retrieval-Augmented Generation (RAG). The article addresses critical operational aspects such as continuous integration, scalable infrastructure, monitoring systems, and user onboarding.

With the MLOps landscape rapidly expanding and evolving, making sense of the various offerings can be incredibly difficult without the right expertise. How do different end-to-end MLOps platforms compare? Which parts of the digital highway do they actually cover? In this blog post we present our proven approach to Data and ML tooling evaluation.

Machine Learning Architects Basel (MLAB) launches a new webinar for The Digital Highway for End-to-End Machine Learning & Effective MLOps, where we will present our blueprint for a digital highway for ML systems to illustrate how organizations can generate sustainable value by building and running reliable machine learning solutions.

Building reliable machine learning systems for the future.

As organizations continue to rely on data-driven decision-making, the reliability and performance of ML models become more and more critical. That's why we are exploring the essential aspects of achieving reliable and scalable machine learning deployments through Site Reliability Engineering (SRE) practices and continuous improvement of ML model delivery.

Building solid machine learning models for the future.

Leverage reliability engineering best practices for data pipelines and successful DataOps, and think holistically about unified data analytics platforms.

This blog post is only the beginning of a whole storyline about reliable Data & ML solutions, where we unfold all the pieces along an end-to-end ML lifecycle.

In this blog post, we present our blueprint for a digital highway for end-to-end machine learning and effective MLOps.

In this blog post we describe the concepts of monitoring and observability and explain how they can be applied to machine learning operations. We aim to give answers to questions such as "Why do I need observability?", "What are some of the unique issues for observability imposed by data and model pipelines?", and "How do I start addressing these challenges?".

Curix, a NextGen resilience AIOps solution proudly announces their strategic partnership with Swiss Digital Network (SND), Digital Architects Zurich (DAZ) and Machine Learning Architects Basel (MLAB), bringing together the powerful AI-based immune system for IT CuriX, with the in-depth method and integration knowledge of Swiss Digital Network. Jointly we will enable AI based big data intelligence and cloud transformations....

It sounds simple but can’t be stressed enough: a project at the intersection of machine learning and healthcare won’t be a success if healthcare professionals are not involved from the very beginning. While it is easy to define ML use cases based on a superficial understanding of the field, their clinical relevance can be judged best by physicians and domain experts. In this blog post, we outline why it is important to closely work with domain experts when developing an ML solution for healthcare and which role MLOps plays.

The digitalization of the healthcare sector turned out to be a double-edged sword. On the one hand, it brought us impressive advances in medical imaging and opened the door to effective remote health monitoring. On the other hand, in some cases, instead of improving healthcare provision, digitalization has led to a significant productivity drain. In this blog post, we highlight why and how healthcare organizations can benefit from current advances in machine learning.

The use of Machine Learning (ML) and its operationalization through the Machine Learning Operations (MLOps) paradigm bring a lot of benefits. Nevertheless, realizing the full potential of AI is challenging and every organization is at a different stage in the process. We devopled a model including four levels from pre-machine learning to functional MLOps.

Effective MLOps is the concept we preach at ML Architects Basel to democratize MLOps and make it accessible to non-high-tech companies and businesses.

It may not be the first time you hear about MLOps. This agile approach, first introduced by Google in 2015 in the famous article “Hidden Technical Debt in Machine Learning”, has since then been at the center of interest of new Machine Learning approaches.

Machine Learning Architects Basel (MLAB) launches an innovative Data Science for Life Sciences (DSfLS) training to help your pharma, biotech and healthcare teams and organizations unlock their Data Science potential.

Great SREs are not hired, they are trained! We are bringing you effective 5-day bootcamps for individuals and customisable enterprise trainings for teams, developed by industry practitioners of the Swiss market leader for Site Reliability Engineering (SRE).

The Swiss Digital Network (SDN), Switzerland’s first independent and open consulting network, is elated to welcome two new innovative cells in Basel and in Bern. Machine Learning Architects Basel and PhiloSolvis contribute innovative, highly effective and supplemental expertise and value propositions to the network.