Scientific Support

Machine learning provides a powerful framework for predictive modeling in scientific research:

• Infer outcomes from complex datasets using classification and regression models
• Evaluate and improve models based on predictive performance
• Use exploratory techniques to better understand and prepare your data

At DCSR, we support researchers in several key areas of machine learning:

Training

We help you understand how specific machine learning methods work and how to apply them in your research. We also offers short introductory courses on machine learning; see ML courses.

Methodology

We assist you in selecting and applying appropriate machine learning methods for your research.

This may include:

• A pilot phase, where we collaboratively develop and test code on your laptop or UNIL clusters.clusters
• A production phase, where we help scale and refine your workflow.workflow

More specifically, we can:

• Identify existing tools suited to your analysis
• Help install and run them on your laptop or UNIL clusters
• Explain key parameters and settings
• Develop custom algorithms and code if no suitable tools exist

Infrastructure

We help you efficiently run your machine learning workflows on UNIL clusters.

This includes:

• Installing and configuring your code
• Profiling performance to optimize resource usage (RAM, CPUs/GPUs, number of nodes)

Collaboration at UNIL

We can connect you with relevant experts at UNIL to discuss specific machine learning challenges.

Example Use Cases:

1. Experimental scientist
   Wants to analyze data using machine learning on a laptop or UNIL clusters.
   → We help identify suitable tools, explain how they work, and support their use.
   
   
2. Data scientist (setup phase)
   Wants to implement a machine learning pipeline but is unsure how to proceed.
   → We help select and apply appropriate methods.
   
   
3. Data scientist (review phase)
   Has implemented a pipeline and wants feedback.
   → We review the methodology and suggest improvements or alternatives.
   
   
4. Scaling from laptop to cluster
   Wants to move a pipeline from a local machine to UNIL clusters.
   → We assist with deployment, software setup, and performance optimization.