Scientific Support

Machine learning is widely used across many scientific fields. For example:

• Unsupervised methods are useful for data exploration and visualization (dimensionality reduction and clustering).
• Supervised methods are used for prediction tasks, where an output is inferred from input data (classification or regression).

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.
• A production phase, where we help scale and refine your 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 can help you toefficiently implement efficientlyrun your machine learning pipelineworkflows on the UNIL clusters.

More

This specifically,includes:

we

• Installing canand helpconfiguring youyour code
• Profiling performance to installoptimize yourresource codes on the UNIL clusters and to profile them in order to find the optimal settingusage (inRAM, terms of RAM,CPUs/GPUs, number of nodesnodes)
and

Collaboration CPU/GPU).

Fosterat UNIL collaborations

We can helpconnect you towith find appropriaterelevant experts at the UNIL to discuss with you about specific machine learning related problems. challenges.

AExample fewUse examples:Cases:

1. An experimental

1. Experimental scientist would like
   Wants to learn how to analyse his/heranalyze data on his/her laptop or on the UNIL clusters by using machine learning methods.on a laptop or UNIL clusters.
   → We can help thisidentify researcher to find appropriate machine learningsuitable tools, to understandexplain how they workwork, and tosupport usetheir themuse.
   
on his/her laptop or on the UNIL clusters.

2. A data

2. Data scientist would(setup likephase)
   Wants to implement a machine learning pipeline (on his/her laptop or on the UNIL clusters) but he/she is not sureunsure how to do it properly.proceed.
   → We can help this researcher to findselect and apply appropriate machinemethods.
   
   
learning methods.

3. A data

3. Data scientist has(review phase)
   Has implemented a machine learning pipeline (on his/her laptop or on the UNIL clusters) and wouldwants likefeedback.
   → toWe discuss aboutreview the methodology he/she used. We can help this researcher to check whether his/her approach is correct and we can also suggest alternativeimprovements approaches.
   or

   4.alternatives.
   

A data scientist has implemented a machine learning pipeline on his/her laptop and would like to run it on the UNIL clusters (going
4. Scaling from laptop to cluster). We can help this researchercluster
   Wants to implementmove efficiently his/hera pipeline onfrom thea local machine to UNIL clusters.
   → ThisWe mayassist involvewith thedeployment, installationsoftware of softwaressetup, and theperformance profilingoptimization.
   
of codes.