Between 22.06 and 23.06 the Swiss Conference on Data Science brings together practitioners, learners and the curious, to broaden their knowledge and their network within the field of analytics. The first day open its doors with a series of workshops from Managing the End-to-End Machine Learning Lifecycle to discovering how to Develop Fair Algorithms. The second day of the conference brings a multitude of seminars at the renowned KKL in Luzern, regarding the business implementation of machine learning models as well as the future trends of the entire domain.
The workshop case study
For Allgeier, the conference offers the opportunity to exchange ideas and keep its analytics team sharp with new business implementations. One of these implementations was a workshop regarding The Full Machine Learning Lifecycle. The workshop developed by Steffen Terhaar, Tim Rohner, Bernhard Venneman, Spyros Cavadias and Roman Moser from the consulting firm D ONE, dives into a Machine Learning (ML) case study, that depicts and practically implements a machine learning operations (MLOps) pipeline from scoping to deployment using open-source tools. Essentially showcasing how the DevOps principles from software engineering translate to data science and machine learning.
To give you a brief overview of what this entails: MLOps is a Machine Learning (ML) engineering practice that aims to unify ML system development (Dev) and ML system operation (Ops). It serves as a counterpart to the DevOps practice in classical software development which involves Continuous Integration (CI) and Continuous Deployment (CD). Practicing MLOps advocates automation and monitoring at all steps of the ML system construction, including integration, testing, releasing, deployment, and infrastructure management.
The The Full Machine Learning Lifecycle workshop brought together approximately 20 data scientist and analyst from all over Europe. The journey starts at the Great Hall of the Metropol Hotel in Zurich, where everyone takes their seats. As the beamer powers on and the D ONE team take center stage, the lecturers explain that the workshop case study focuses on building and productionalizing a machine learning model to predict turbine malfunctions from a dataset produced by Winji.
To understand why this dataset is so interesting, here is a brief overview of what Winji does: the Zurich-based data-driven company offers a platform that provides AI-based insights from alternative energy assembly components and environmental factors, that provide wind and solar farms with useful recommendations and accurate forecasting to optimize the resources overall output.
It should be noted that the workshop does not put any focus on what sort of ML algorithm is being implemented. In the end, this is not relevant. From supervised regression to unsupervised classification models, every application is different. The focus is always on the entirety of the pipeline and its respective steps within the MLOps life cycle.
As the lecturers conclude their explanations and the participants gain a good overview of the provided data, three important facts come to light: